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J Internet Serv Appl (2010) 1: 7–18
DOI 10.1007/s13174-010-0007-6

 O R I G I N A L PA P E R S



Cloud computing: state-of-the-art and research challenges
Qi Zhang · Lu Cheng · Raouf Boutaba




Received: 8 January 2010 / Accepted: 25 February 2010 / Published online: 20 April 2010
© The Brazilian Computer Society 2010


Abstract Cloud computing has recently emerged as a new                  called cloud computing, in which resources (e.g., CPU and
paradigm for hosting and delivering services over the Inter-            storage) are provided as general utilities that can be leased
net. Cloud computing is attractive to business owners as it             and released by users through the Internet in an on-demand
eliminates the requirement for users to plan ahead for pro-             fashion. In a cloud computing environment, the traditional
visioning, and allows enterprises to start from the small and           role of service provider is divided into two: the infrastruc-
increase resources only when there is a rise in service de-             ture providers who manage cloud platforms and lease re-
mand. However, despite the fact that cloud computing offers             sources according to a usage-based pricing model, and ser-
huge opportunities to the IT industry, the development of               vice providers, who rent resources from one or many in-
cloud computing technology is currently at its infancy, with            frastructure providers to serve the end users. The emer-
many issues still to be addressed. In this paper, we present a          gence of cloud computing has made a tremendous impact
survey of cloud computing, highlighting its key concepts,               on the Information Technology (IT) industry over the past
architectural principles, state-of-the-art implementation as            few years, where large companies such as Google, Ama-
well as research challenges. The aim of this paper is to pro-           zon and Microsoft strive to provide more powerful, reliable
vide a better understanding of the design challenges of cloud           and cost-efficient cloud platforms, and business enterprises
computing and identify important research directions in this            seek to reshape their business models to gain benefit from
increasingly important area.                                            this new paradigm. Indeed, cloud computing provides sev-
                                                                        eral compelling features that make it attractive to business
Keywords Cloud computing · Data centers · Virtualization                owners, as shown below.
                                                                           No up-front investment: Cloud computing uses a pay-as-
                                                                        you-go pricing model. A service provider does not need to
1 Introduction                                                          invest in the infrastructure to start gaining benefit from cloud
                                                                        computing. It simply rents resources from the cloud accord-
With the rapid development of processing and storage tech-              ing to its own needs and pay for the usage.
nologies and the success of the Internet, computing re-                    Lowering operating cost: Resources in a cloud environ-
sources have become cheaper, more powerful and more                     ment can be rapidly allocated and de-allocated on demand.
ubiquitously available than ever before. This technological             Hence, a service provider no longer needs to provision ca-
trend has enabled the realization of a new computing model              pacities according to the peak load. This provides huge sav-
                                                                        ings since resources can be released to save on operating
                                                                        costs when service demand is low.
Q. Zhang · L. Cheng · R. Boutaba ( )
University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1                 Highly scalable: Infrastructure providers pool large
e-mail: rboutaba@cs.uwaterloo.ca                                        amount of resources from data centers and make them easily
Q. Zhang                                                                accessible. A service provider can easily expand its service
e-mail: q8zhang@cs.uwaterloo.ca                                         to large scales in order to handle rapid increase in service
L. Cheng                                                                demands (e.g., flash-crowd effect). This model is sometimes
e-mail: l32cheng@cs.uwaterloo.ca                                        called surge computing [5].
8                                                                                                J Internet Serv Appl (2010) 1: 7–18

    Easy access: Services hosted in the cloud are generally        has generated not only market hypes, but also a fair amount
web-based. Therefore, they are easily accessible through a         of skepticism and confusion. For this reason, recently there
variety of devices with Internet connections. These devices        has been work on standardizing the definition of cloud com-
not only include desktop and laptop computers, but also cell       puting. As an example, the work in [49] compared over 20
phones and PDAs.                                                   different definitions from a variety of sources to confirm a
    Reducing business risks and maintenance expenses: By           standard definition. In this paper, we adopt the definition
outsourcing the service infrastructure to the clouds, a service    of cloud computing provided by The National Institute of
provider shifts its business risks (such as hardware failures)     Standards and Technology (NIST) [36], as it covers, in our
to infrastructure providers, who often have better expertise       opinion, all the essential aspects of cloud computing:
and are better equipped for managing these risks. In addi-
tion, a service provider can cut down the hardware mainte-         NIST definition of cloud computing Cloud computing is a
nance and the staff training costs.                                model for enabling convenient, on-demand network access
    However, although cloud computing has shown consid-            to a shared pool of configurable computing resources (e.g.,
erable opportunities to the IT industry, it also brings many       networks, servers, storage, applications, and services) that
unique challenges that need to be carefully addressed. In this     can be rapidly provisioned and released with minimal man-
paper, we present a survey of cloud computing, highlighting        agement effort or service provider interaction.
its key concepts, architectural principles, state-of-the-art im-
plementations as well as research challenges. Our aim is to            The main reason for the existence of different percep-
provide a better understanding of the design challenges of         tions of cloud computing is that cloud computing, unlike
cloud computing and identify important research directions         other technical terms, is not a new technology, but rather
in this fascinating topic.                                         a new operations model that brings together a set of ex-
    The remainder of this paper is organized as follows. In        isting technologies to run business in a different way. In-
Sect. 2 we provide an overview of cloud computing and              deed, most of the technologies used by cloud computing,
compare it with other related technologies. In Sect. 3, we         such as virtualization and utility-based pricing, are not new.
describe the architecture of cloud computing and present           Instead, cloud computing leverages these existing technolo-
                                                                   gies to meet the technological and economic requirements
its design principles. The key features and characteristics of
                                                                   of today’s demand for information technology.
cloud computing are detailed in Sect. 4. Section 5 surveys
the commercial products as well as the current technologies
                                                                   2.2 Related technologies
used for cloud computing. In Sect. 6, we summarize the cur-
rent research topics in cloud computing. Finally, the paper        Cloud computing is often compared to the following tech-
concludes in Sect. 7.                                              nologies, each of which shares certain aspects with cloud
                                                                   computing:
                                                                       Grid Computing: Grid computing is a distributed com-
2 Overview of cloud computing                                      puting paradigm that coordinates networked resources to
                                                                   achieve a common computational objective. The develop-
This section presents a general overview of cloud comput-          ment of Grid computing was originally driven by scien-
ing, including its definition and a comparison with related         tific applications which are usually computation-intensive.
concepts.                                                          Cloud computing is similar to Grid computing in that it also
                                                                   employs distributed resources to achieve application-level
2.1 Definitions                                                     objectives. However, cloud computing takes one step further
                                                                   by leveraging virtualization technologies at multiple levels
The main idea behind cloud computing is not a new one.             (hardware and application platform) to realize resource shar-
John McCarthy in the 1960s already envisioned that com-            ing and dynamic resource provisioning.
puting facilities will be provided to the general public like          Utility Computing: Utility computing represents the
a utility [39]. The term “cloud” has also been used in vari-       model of providing resources on-demand and charging cus-
ous contexts such as describing large ATM networks in the          tomers based on usage rather than a flat rate. Cloud comput-
1990s. However, it was after Google’s CEO Eric Schmidt             ing can be perceived as a realization of utility computing. It
used the word to describe the business model of provid-            adopts a utility-based pricing scheme entirely for economic
ing services across the Internet in 2006, that the term re-        reasons. With on-demand resource provisioning and utility-
ally started to gain popularity. Since then, the term cloud        based pricing, service providers can truly maximize resource
computing has been used mainly as a marketing term in a            utilization and minimize their operating costs.
variety of contexts to represent many different ideas. Cer-            Virtualization: Virtualization is a technology that ab-
tainly, the lack of a standard definition of cloud computing        stracts away the details of physical hardware and provides
J Internet Serv Appl (2010) 1: 7–18                                                                                          9

virtualized resources for high-level applications. A virtual-        The hardware layer: This layer is responsible for man-
ized server is commonly called a virtual machine (VM). Vir-      aging the physical resources of the cloud, including phys-
tualization forms the foundation of cloud computing, as it       ical servers, routers, switches, power and cooling systems.
provides the capability of pooling computing resources from      In practice, the hardware layer is typically implemented
clusters of servers and dynamically assigning or reassigning     in data centers. A data center usually contains thousands
virtual resources to applications on-demand.                     of servers that are organized in racks and interconnected
   Autonomic Computing: Originally coined by IBM in              through switches, routers or other fabrics. Typical issues
2001, autonomic computing aims at building computing sys-        at hardware layer include hardware configuration, fault-
tems capable of self-management, i.e. reacting to internal       tolerance, traffic management, power and cooling resource
and external observations without human intervention. The        management.
goal of autonomic computing is to overcome the manage-               The infrastructure layer: Also known as the virtualiza-
ment complexity of today’s computer systems. Although            tion layer, the infrastructure layer creates a pool of storage
cloud computing exhibits certain autonomic features such         and computing resources by partitioning the physical re-
as automatic resource provisioning, its objective is to lower    sources using virtualization technologies such as Xen [55],
the resource cost rather than to reduce system complexity.       KVM [30] and VMware [52]. The infrastructure layer is an
   In summary, cloud computing leverages virtualization          essential component of cloud computing, since many key
technology to achieve the goal of providing computing re-        features, such as dynamic resource assignment, are only
sources as a utility. It shares certain aspects with grid com-   made available through virtualization technologies.
puting and autonomic computing but differs from them in              The platform layer: Built on top of the infrastructure
other aspects. Therefore, it offers unique benefits and im-       layer, the platform layer consists of operating systems and
poses distinctive challenges to meet its requirements.           application frameworks. The purpose of the platform layer
                                                                 is to minimize the burden of deploying applications directly
                                                                 into VM containers. For example, Google App Engine oper-
                                                                 ates at the platform layer to provide API support for imple-
3 Cloud computing architecture
                                                                 menting storage, database and business logic of typical web
                                                                 applications.
This section describes the architectural, business and various       The application layer: At the highest level of the hierar-
operation models of cloud computing.                             chy, the application layer consists of the actual cloud appli-
                                                                 cations. Different from traditional applications, cloud appli-
3.1 A layered model of cloud computing                           cations can leverage the automatic-scaling feature to achieve
                                                                 better performance, availability and lower operating cost.
Generally speaking, the architecture of a cloud comput-              Compared to traditional service hosting environments
ing environment can be divided into 4 layers: the hard-          such as dedicated server farms, the architecture of cloud
ware/datacenter layer, the infrastructure layer, the platform    computing is more modular. Each layer is loosely coupled
layer and the application layer, as shown in Fig. 1. We de-      with the layers above and below, allowing each layer to
scribe each of them in detail:                                   evolve separately. This is similar to the design of the OSI


Fig. 1 Cloud computing
architecture
10                                                                                              J Internet Serv Appl (2010) 1: 7–18

model for network protocols. The architectural modularity        3.3 Types of clouds
allows cloud computing to support a wide range of applica-
tion requirements while reducing management and mainte-          There are many issues to consider when moving an enter-
nance overhead.                                                  prise application to the cloud environment. For example,
                                                                 some service providers are mostly interested in lowering op-
3.2 Business model                                               eration cost, while others may prefer high reliability and se-
                                                                 curity. Accordingly, there are different types of clouds, each
                                                                 with its own benefits and drawbacks:
Cloud computing employs a service-driven business model.
                                                                     Public clouds: A cloud in which service providers of-
In other words, hardware and platform-level resources are        fer their resources as services to the general public. Pub-
provided as services on an on-demand basis. Conceptually,        lic clouds offer several key benefits to service providers, in-
every layer of the architecture described in the previous sec-   cluding no initial capital investment on infrastructure and
tion can be implemented as a service to the layer above.         shifting of risks to infrastructure providers. However, pub-
Conversely, every layer can be perceived as a customer of        lic clouds lack fine-grained control over data, network and
the layer below. However, in practice, clouds offer services     security settings, which hampers their effectiveness in many
that can be grouped into three categories: software as a ser-    business scenarios.
vice (SaaS), platform as a service (PaaS), and infrastructure        Private clouds: Also known as internal clouds, private
as a service (IaaS).                                             clouds are designed for exclusive use by a single organiza-
1. Infrastructure as a Service: IaaS refers to on-demand         tion. A private cloud may be built and managed by the orga-
                                                                 nization or by external providers. A private cloud offers the
   provisioning of infrastructural resources, usually in terms
                                                                 highest degree of control over performance, reliability and
   of VMs. The cloud owner who offers IaaS is called an
                                                                 security. However, they are often criticized for being simi-
   IaaS provider. Examples of IaaS providers include Ama-
                                                                 lar to traditional proprietary server farms and do not provide
   zon EC2 [2], GoGrid [15] and Flexiscale [18].
                                                                 benefits such as no up-front capital costs.
2. Platform as a Service: PaaS refers to providing platform
                                                                     Hybrid clouds: A hybrid cloud is a combination of public
   layer resources, including operating system support and
                                                                 and private cloud models that tries to address the limitations
   software development frameworks. Examples of PaaS
                                                                 of each approach. In a hybrid cloud, part of the service in-
   providers include Google App Engine [20], Microsoft
                                                                 frastructure runs in private clouds while the remaining part
   Windows Azure [53] and Force.com [41].
                                                                 runs in public clouds. Hybrid clouds offer more flexibility
3. Software as a Service: SaaS refers to providing on-           than both public and private clouds. Specifically, they pro-
   demand applications over the Internet. Examples of SaaS       vide tighter control and security over application data com-
   providers include Salesforce.com [41], Rackspace [17]         pared to public clouds, while still facilitating on-demand
   and SAP Business ByDesign [44].                               service expansion and contraction. On the down side, de-
   The business model of cloud computing is depicted by          signing a hybrid cloud requires carefully determining the
Fig. 2. According to the layered architecture of cloud com-      best split between public and private cloud components.
puting, it is entirely possible that a PaaS provider runs its        Virtual Private Cloud: An alternative solution to address-
cloud on top of an IaaS provider’s cloud. However, in the        ing the limitations of both public and private clouds is called
current practice, IaaS and PaaS providers are often parts of     Virtual Private Cloud (VPC). A VPC is essentially a plat-
the same organization (e.g., Google and Salesforce). This is     form running on top of public clouds. The main difference is
                                                                 that a VPC leverages virtual private network (VPN) technol-
why PaaS and IaaS providers are often called the infrastruc-
                                                                 ogy that allows service providers to design their own topol-
ture providers or cloud providers [5].
                                                                 ogy and security settings such as firewall rules. VPC is es-
                                                                 sentially a more holistic design since it not only virtualizes
                                                                 servers and applications, but also the underlying commu-
                                                                 nication network as well. Additionally, for most companies,
                                                                 VPC provides seamless transition from a proprietary service
                                                                 infrastructure to a cloud-based infrastructure, owing to the
                                                                 virtualized network layer.
                                                                     For most service providers, selecting the right cloud
                                                                 model is dependent on the business scenario. For exam-
                                                                 ple, computation-intensive scientific applications are best
                                                                 deployed on public clouds for cost-effectiveness. Arguably,
Fig. 2 Business model of cloud computing                         certain types of clouds will be more popular than others.
J Internet Serv Appl (2010) 1: 7–18                                                                                           11

In particular, it was predicted that hybrid clouds will be the       Self-organizing: Since resources can be allocated or de-
dominant type for most organizations [14]. However, vir-          allocated on-demand, service providers are empowered to
tual private clouds have started to gain more popularity since    manage their resource consumption according to their own
their inception in 2009.                                          needs. Furthermore, the automated resource management
                                                                  feature yields high agility that enables service providers to
                                                                  respond quickly to rapid changes in service demand such as
4 Cloud computing characteristics                                 the flash crowd effect.
                                                                     Utility-based pricing: Cloud computing employs a pay-
Cloud computing provides several salient features that are        per-use pricing model. The exact pricing scheme may vary
different from traditional service computing, which we sum-       from service to service. For example, a SaaS provider may
marize below:                                                     rent a virtual machine from an IaaS provider on a per-hour
    Multi-tenancy: In a cloud environment, services owned         basis. On the other hand, a SaaS provider that provides
by multiple providers are co-located in a single data center.     on-demand customer relationship management (CRM) may
The performance and management issues of these services           charge its customers based on the number of clients it serves
are shared among service providers and the infrastructure         (e.g., Salesforce). Utility-based pricing lowers service oper-
provider. The layered architecture of cloud computing pro-        ating cost as it charges customers on a per-use basis. How-
vides a natural division of responsibilities: the owner of each   ever, it also introduces complexities in controlling the oper-
layer only needs to focus on the specific objectives associ-
                                                                  ating cost. In this perspective, companies like VKernel [51]
ated with this layer. However, multi-tenancy also introduces
                                                                  provide software to help cloud customers understand, ana-
difficulties in understanding and managing the interactions
                                                                  lyze and cut down the unnecessary cost on resource con-
among various stakeholders.
                                                                  sumption.
    Shared resource pooling: The infrastructure provider of-
fers a pool of computing resources that can be dynamically
assigned to multiple resource consumers. Such dynamic re-
                                                                  5 State-of-the-art
source assignment capability provides much flexibility to in-
frastructure providers for managing their own resource us-
                                                                  In this section, we present the state-of-the-art implementa-
age and operating costs. For instance, an IaaS provider can
                                                                  tions of cloud computing. We first describe the key technolo-
leverage VM migration technology to attain a high degree
of server consolidation, hence maximizing resource utiliza-       gies currently used for cloud computing. Then, we survey
tion while minimizing cost such as power consumption and          the popular cloud computing products.
cooling.
    Geo-distribution and ubiquitous network access: Clouds        5.1 Cloud computing technologies
are generally accessible through the Internet and use the
Internet as a service delivery network. Hence any device          This section provides a review of technologies used in cloud
with Internet connectivity, be it a mobile phone, a PDA or        computing environments.
a laptop, is able to access cloud services. Additionally, to
achieve high network performance and localization, many           5.1.1 Architectural design of data centers
of today’s clouds consist of data centers located at many
locations around the globe. A service provider can easily         A data center, which is home to the computation power and
leverage geo-diversity to achieve maximum service utility.        storage, is central to cloud computing and contains thou-
    Service oriented: As mentioned previously, cloud com-         sands of devices like servers, switches and routers. Proper
puting adopts a service-driven operating model. Hence it          planning of this network architecture is critical, as it will
places a strong emphasis on service management. In a cloud,       heavily influence applications performance and throughput
each IaaS, PaaS and SaaS provider offers its service accord-      in such a distributed computing environment. Further, scala-
ing to the Service Level Agreement (SLA) negotiated with          bility and resiliency features need to be carefully considered.
its customers. SLA assurance is therefore a critical objective        Currently, a layered approach is the basic foundation of
of every provider.                                                the network architecture design, which has been tested in
    Dynamic resource provisioning: One of the key features        some of the largest deployed data centers. The basic layers
of cloud computing is that computing resources can be ob-         of a data center consist of the core, aggregation, and access
tained and released on the fly. Compared to the traditional        layers, as shown in Fig. 3. The access layer is where the
model that provisions resources according to peak demand,         servers in racks physically connect to the network. There
dynamic resource provisioning allows service providers to         are typically 20 to 40 servers per rack, each connected to an
acquire resources based on the current demand, which can          access switch with a 1 Gbps link. Access switches usually
considerably lower the operating cost.                            connect to two aggregation switches for redundancy with
12                                                                                                J Internet Serv Appl (2010) 1: 7–18

                                                                       Scalability: The network infrastructure must be able to
                                                                    scale to a large number of servers and allow for incremental
                                                                    expansion.
                                                                       Backward compatibility: The network infrastructure
                                                                    should be backward compatible with switches and routers
                                                                    running Ethernet and IP. Because existing data centers have
                                                                    commonly leveraged commodity Ethernet and IP based de-
                                                                    vices, they should also be used in the new architecture with-
                                                                    out major modifications.
                                                                       Another area of rapid innovation in the industry is the de-
                                                                    sign and deployment of shipping-container based, modular
                                                                    data center (MDC). In an MDC, normally up to a few thou-
                                                                    sands of servers, are interconnected via switches to form
                                                                    the network infrastructure. Highly interactive applications,
                                                                    which are sensitive to response time, are suitable for geo-
Fig. 3 Basic layered design of data center network infrastructure   diverse MDC placed close to major population areas. The
                                                                    MDC also helps with redundancy because not all areas are
                                                                    likely to lose power, experience an earthquake, or suffer ri-
10 Gbps links. The aggregation layer usually provides im-           ots at the same time. Rather than the three-layered approach
portant functions, such as domain service, location service,        discussed above, Guo et al. [22, 23] proposed server-centric,
server load balancing, and more. The core layer provides            recursively defined network structures of MDC.
connectivity to multiple aggregation switches and provides
a resilient routed fabric with no single point of failure. The      5.1.2 Distributed file system over clouds
core routers manage traffic into and out of the data center.
    A popular practice is to leverage commodity Ethernet            Google File System (GFS) [19] is a proprietary distributed
switches and routers to build the network infrastructure. In        file system developed by Google and specially designed to
different business solutions, the layered network infrastruc-       provide efficient, reliable access to data using large clusters
ture can be elaborated to meet specific business challenges.         of commodity servers. Files are divided into chunks of 64
Basically, the design of a data center network architecture         megabytes, and are usually appended to or read and only
should meet the following objectives [1, 21–23, 35]:                extremely rarely overwritten or shrunk. Compared with tra-
    Uniform high capacity: The maximum rate of a server-            ditional file systems, GFS is designed and optimized to run
to-server traffic flow should be limited only by the available        on data centers to provide extremely high data throughputs,
capacity on the network-interface cards of the sending and          low latency and survive individual server failures.
receiving servers, and assigning servers to a service should            Inspired by GFS, the open source Hadoop Distributed
be independent of the network topology. It should be possi-         File System (HDFS) [24] stores large files across multi-
ble for an arbitrary host in the data center to communicate         ple machines. It achieves reliability by replicating the data
with any other host in the network at the full bandwidth of         across multiple servers. Similarly to GFS, data is stored on
                                                                    multiple geo-diverse nodes. The file system is built from a
its local network interface.
                                                                    cluster of data nodes, each of which serves blocks of data
    Free VM migration: Virtualization allows the entire VM
                                                                    over the network using a block protocol specific to HDFS.
state to be transmitted across the network to migrate a VM
                                                                    Data is also provided over HTTP, allowing access to all con-
from one physical machine to another. A cloud comput-
                                                                    tent from a web browser or other types of clients. Data nodes
ing hosting service may migrate VMs for statistical multi-
                                                                    can talk to each other to rebalance data distribution, to move
plexing or dynamically changing communication patterns
                                                                    copies around, and to keep the replication of data high.
to achieve high bandwidth for tightly coupled hosts or to
achieve variable heat distribution and power availability in        5.1.3 Distributed application framework over clouds
the data center. The communication topology should be de-
signed so as to support rapid virtual machine migration.            HTTP-based applications usually conform to some web ap-
    Resiliency: Failures will be common at scale. The net-          plication framework such as Java EE. In modern data center
work infrastructure must be fault-tolerant against various          environments, clusters of servers are also used for computa-
types of server failures, link outages, or server-rack failures.    tion and data-intensive jobs such as financial trend analysis,
Existing unicast and multicast communications should not            or film animation.
be affected to the extent allowed by the underlying physical           MapReduce [16] is a software framework introduced by
connectivity.                                                       Google to support distributed computing on large data sets
J Internet Serv Appl (2010) 1: 7–18                                                                                           13

on clusters of computers. MapReduce consists of one Mas-          data as “objects” that are grouped in “buckets.” Each object
ter, to which client applications submit MapReduce jobs.          contains from 1 byte to 5 gigabytes of data. Object names
The Master pushes work out to available task nodes in the         are essentially URI [6] pathnames. Buckets must be explic-
data center, striving to keep the tasks as close to the data      itly created before they can be used. A bucket can be stored
as possible. The Master knows which node contains the             in one of several Regions. Users can choose a Region to opti-
data, and which other hosts are nearby. If the task cannot        mize latency, minimize costs, or address regulatory require-
be hosted on the node where the data is stored, priority is       ments.
given to nodes in the same rack. In this way, network traffic          Amazon Virtual Private Cloud (VPC) is a secure and
on the main backbone is reduced, which also helps to im-          seamless bridge between a company’s existing IT infrastruc-
prove throughput, as the backbone is usually the bottleneck.      ture and the AWS cloud. Amazon VPC enables enterprises
If a task fails or times out, it is rescheduled. If the Master    to connect their existing infrastructure to a set of isolated
fails, all ongoing tasks are lost. The Master records what it     AWS compute resources via a Virtual Private Network
is up to in the filesystem. When it starts up, it looks for any    (VPN) connection, and to extend their existing management
such data, so that it can restart work from where it left off.    capabilities such as security services, firewalls, and intrusion
    The open source Hadoop MapReduce project [25] is in-          detection systems to include their AWS resources.
spired by Google’s work. Currently, many organizations are            For cloud users, Amazon CloudWatch is a useful man-
using Hadoop MapReduce to run large data-intensive com-           agement tool which collects raw data from partnered AWS
putations.                                                        services such as Amazon EC2 and then processes the in-
                                                                  formation into readable, near real-time metrics. The metrics
5.2 Commercial products                                           about EC2 include, for example, CPU utilization, network
                                                                  in/out bytes, disk read/write operations, etc.
In this section, we provide a survey of some of the dominant
cloud computing products.
                                                                  5.2.2 Microsoft Windows Azure platform
5.2.1 Amazon EC2
                                                                  Microsoft’s Windows Azure platform [53] consists of three
Amazon Web Services (AWS) [3] is a set of cloud services,         components and each of them provides a specific set of ser-
providing cloud-based computation, storage and other func-        vices to cloud users. Windows Azure provides a Windows-
tionality that enable organizations and individuals to deploy     based environment for running applications and storing data
applications and services on an on-demand basis and at com-       on servers in data centers; SQL Azure provides data services
modity prices. Amazon Web Services’ offerings are acces-          in the cloud based on SQL Server; and .NET Services offer
sible over HTTP, using REST and SOAP protocols.                   distributed infrastructure services to cloud-based and local
    Amazon Elastic Compute Cloud (Amazon EC2) enables             applications. Windows Azure platform can be used both by
cloud users to launch and manage server instances in data         applications running in the cloud and by applications run-
centers using APIs or available tools and utilities. EC2 in-      ning on local systems.
stances are virtual machines running on top of the Xen virtu-         Windows Azure also supports applications built on the
alization engine [55]. After creating and starting an instance,   .NET Framework and other ordinary languages supported in
users can upload software and make changes to it. When            Windows systems, like C#, Visual Basic, C++, and others.
changes are finished, they can be bundled as a new machine         Windows Azure supports general-purpose programs, rather
image. An identical copy can then be launched at any time.        than a single class of computing. Developers can create web
Users have nearly full control of the entire software stack       applications using technologies such as ASP.NET and Win-
on the EC2 instances that look like hardware to them. On          dows Communication Foundation (WCF), applications that
the other hand, this feature makes it inherently difficult for     run as independent background processes, or applications
Amazon to offer automatic scaling of resources.                   that combine the two. Windows Azure allows storing data
    EC2 provides the ability to place instances in multiple lo-   in blobs, tables, and queues, all accessed in a RESTful style
cations. EC2 locations are composed of Regions and Avail-         via HTTP or HTTPS.
ability Zones. Regions consist of one or more Availability            SQL Azure components are SQL Azure Database and
Zones, are geographically dispersed. Availability Zones are       “Huron” Data Sync. SQL Azure Database is built on Mi-
distinct locations that are engineered to be insulated from       crosoft SQL Server, providing a database management sys-
failures in other Availability Zones and provide inexpensive,     tem (DBMS) in the cloud. The data can be accessed using
low latency network connectivity to other Availability Zones      ADO.NET and other Windows data access interfaces. Users
in the same Region.                                               can also use on-premises software to work with this cloud-
    EC2 machine images are stored in and retrieved from           based information. “Huron” Data Sync synchronizes rela-
Amazon Simple Storage Service (Amazon S3). S3 stores              tional data across various on-premises DBMSs.
14                                                                                                 J Internet Serv Appl (2010) 1: 7–18

Table 1 A comparison of representative commercial products

Cloud Provider                    Amazon EC2                       Windows Azure                         Google App Engine

Classes of Utility Computing      Infrastructure service           Platform service                      Platform service

Target Applications               General-purpose applications     General-purpose Windows               Traditional web applications
                                                                   applications                          with supported framework

Computation                       OS Level on a Xen Virtual        Microsoft Common Language             Predefined web application
                                  Machine                          Runtime (CLR) VM; Predefined           frameworks
                                                                   roles of app. instances

Storage                           Elastic Block Store; Amazon      Azure storage service and SQL         BigTable and MegaStore
                                  Simple Storage Service (S3);     Data Services
                                  Amazon SimpleDB

Auto Scaling                      Automatically changing the       Automatic scaling based on            Automatic Scaling which is
                                  number of instances based on     application roles and a               transparent to users
                                  parameters that users specify    configuration file specified by
                                                                   users



   The .NET Services facilitate the creation of distributed       and management of the resources. Users can choose one
applications. The Access Control component provides a             type or combinations of several types of cloud offerings to
cloud-based implementation of single identity verification         satisfy specific business requirements.
across applications and companies. The Service Bus helps
an application expose web services endpoints that can be
accessed by other applications, whether on-premises or in         6 Research challenges
the cloud. Each exposed endpoint is assigned a URI, which
clients can use to locate and access a service.                   Although cloud computing has been widely adopted by the
   All of the physical resources, VMs and applications in         industry, the research on cloud computing is still at an early
the data center are monitored by software called the fabric       stage. Many existing issues have not been fully addressed,
controller. With each application, the users upload a config-      while new challenges keep emerging from industry applica-
uration file that provides an XML-based description of what        tions. In this section, we summarize some of the challenging
the application needs. Based on this file, the fabric controller   research issues in cloud computing.
decides where new applications should run, choosing phys-
ical servers to optimize hardware utilization.                    6.1 Automated service provisioning

5.2.3 Google App Engine                                           One of the key features of cloud computing is the capabil-
                                                                  ity of acquiring and releasing resources on-demand. The ob-
Google App Engine [20] is a platform for traditional web          jective of a service provider in this case is to allocate and
applications in Google-managed data centers. Currently, the       de-allocate resources from the cloud to satisfy its service
supported programming languages are Python and Java.              level objectives (SLOs), while minimizing its operational
Web frameworks that run on the Google App Engine include          cost. However, it is not obvious how a service provider can
Django, CherryPy, Pylons, and web2py, as well as a custom         achieve this objective. In particular, it is not easy to de-
Google-written web application framework similar to JSP           termine how to map SLOs such as QoS requirements to
or ASP.NET. Google handles deploying code to a cluster,           low-level resource requirement such as CPU and memory
monitoring, failover, and launching application instances as      requirements. Furthermore, to achieve high agility and re-
necessary. Current APIs support features such as storing and      spond to rapid demand fluctuations such as in flash crowd
retrieving data from a BigTable [10] non-relational database,     effect, the resource provisioning decisions must be made on-
making HTTP requests and caching. Developers have read-           line.
only access to the filesystem on App Engine.                           Automated service provisioning is not a new problem.
   Table 1 summarizes the three examples of popular cloud         Dynamic resource provisioning for Internet applications has
offerings in terms of the classes of utility computing, tar-      been studied extensively in the past [47, 57]. These ap-
get types of application, and more importantly their models       proaches typically involve: (1) Constructing an application
of computation, storage and auto-scaling. Apparently, these       performance model that predicts the number of application
cloud offerings are based on different levels of abstraction      instances required to handle demand at each particular level,
J Internet Serv Appl (2010) 1: 7–18                                                                                         15

in order to satisfy QoS requirements; (2) Periodically pre-       communication requirements, have also been considered re-
dicting future demand and determining resource require-           cently [34].
ments using the performance model; and (3) Automatically             However, server consolidation activities should not hurt
allocating resources using the predicted resource require-        application performance. It is known that the resource usage
ments. Application performance model can be constructed           (also known as the footprint [45]) of individual VMs may
using various techniques, including Queuing theory [47],          vary over time [54]. For server resources that are shared
Control theory [28] and Statistical Machine Learning [7].         among VMs, such as bandwidth, memory cache and disk
   Additionally, there is a distinction between proactive         I/O, maximally consolidating a server may result in re-
and reactive resource control. The proactive approach uses        source congestion when a VM changes its footprint on the
predicted demand to periodically allocate resources before        server [38]. Hence, it is sometimes important to observe
they are needed. The reactive approach reacts to immedi-          the fluctuations of VM footprints and use this information
ate demand fluctuations before periodic demand prediction          for effective server consolidation. Finally, the system must
is available. Both approaches are important and necessary         quickly react to resource congestions when they occur [54].
for effective resource control in dynamic operating environ-
ments.                                                            6.4 Energy management

6.2 Virtual machine migration                                     Improving energy efficiency is another major issue in cloud
                                                                  computing. It has been estimated that the cost of powering
Virtualization can provide significant benefits in cloud com-       and cooling accounts for 53% of the total operational expen-
puting by enabling virtual machine migration to balance           diture of data centers [26]. In 2006, data centers in the US
load across the data center. In addition, virtual machine mi-     consumed more than 1.5% of the total energy generated in
gration enables robust and highly responsive provisioning in      that year, and the percentage is projected to grow 18% an-
data centers.                                                     nually [33]. Hence infrastructure providers are under enor-
   Virtual machine migration has evolved from process             mous pressure to reduce energy consumption. The goal is
migration techniques [37]. More recently, Xen [55] and            not only to cut down energy cost in data centers, but also to
VMWare [52] have implemented “live” migration of VMs              meet government regulations and environmental standards.
that involves extremely short downtimes ranging from tens             Designing energy-efficient data centers has recently re-
of milliseconds to a second. Clark et al. [13] pointed out that   ceived considerable attention. This problem can be ap-
migrating an entire OS and all of its applications as one unit    proached from several directions. For example, energy-
allows to avoid many of the difficulties faced by process-         efficient hardware architecture that enables slowing down
level migration approaches, and analyzed the benefits of live      CPU speeds and turning off partial hardware components
migration of VMs.                                                 [8] has become commonplace. Energy-aware job schedul-
   The major benefits of VM migration is to avoid hotspots;        ing [50] and server consolidation [46] are two other ways to
however, this is not straightforward. Currently, detecting        reduce power consumption by turning off unused machines.
workload hotspots and initiating a migration lacks the agility    Recent research has also begun to study energy-efficient net-
to respond to sudden workload changes. Moreover, the in-          work protocols and infrastructures [27]. A key challenge in
memory state should be transferred consistently and effi-          all the above methods is to achieve a good trade-off between
ciently, with integrated consideration of resources for appli-    energy savings and application performance. In this respect,
cations and physical servers.                                     few researchers have recently started to investigate coordi-
                                                                  nated solutions for performance and power management in
6.3 Server consolidation                                          a dynamic cloud environment [32].

Server consolidation is an effective approach to maximize         6.5 Traffic management and analysis
resource utilization while minimizing energy consumption
in a cloud computing environment. Live VM migration tech-         Analysis of data traffic is important for today’s data cen-
nology is often used to consolidate VMs residing on multi-        ters. For example, many web applications rely on analysis
ple under-utilized servers onto a single server, so that the      of traffic data to optimize customer experiences. Network
remaining servers can be set to an energy-saving state. The       operators also need to know how traffic flows through the
problem of optimally consolidating servers in a data center       network in order to make many of the management and plan-
is often formulated as a variant of the vector bin-packing        ning decisions.
problem [11], which is an NP-hard optimization problem.              However, there are several challenges for existing traf-
Various heuristics have been proposed for this problem            fic measurement and analysis methods in Internet Service
[33, 46]. Additionally, dependencies among VMs, such as           Providers (ISPs) networks and enterprise to extend to data
16                                                                                                J Internet Serv Appl (2010) 1: 7–18

centers. Firstly, the density of links is much higher than         applications leverage MapReduce frameworks such as
that in ISPs or enterprise networks, which makes the worst-        Hadoop for scalable and fault-tolerant data processing. Re-
case scenario for existing methods. Secondly, most existing        cent work has shown that the performance and resource con-
methods can compute traffic matrices between a few hun-             sumption of a MapReduce job is highly dependent on the
dreds end hosts, but even a modular data center can have           type of the application [29, 42, 56]. For instance, Hadoop
several thousand servers. Finally, existing methods usually        tasks such as sort is I/O intensive, whereas grep requires
assume some flow patterns that are reasonable in Internet           significant CPU resources. Furthermore, the VM allocated
and enterprises networks, but the applications deployed on         to each Hadoop node may have heterogeneous character-
data centers, such as MapReduce jobs, significantly change          istics. For example, the bandwidth available to a VM is
the traffic pattern. Further, there is tighter coupling in appli-   dependent on other VMs collocated on the same server.
cation’s use of network, computing, and storage resources,         Hence, it is possible to optimize the performance and cost
than what is seen in other settings.                               of a MapReduce application by carefully selecting its con-
   Currently, there is not much work on measurement and            figuration parameter values [29] and designing more effi-
analysis of data center traffic. Greenberg et al. [21] report       cient scheduling algorithms [42, 56]. By mitigating the bot-
data center traffic characteristics on flow sizes and concur-        tleneck resources, execution time of applications can be
rent flows, and use these to guide network infrastructure de-       significantly improved. The key challenges include perfor-
sign. Benson et al. [16] perform a complementary study of          mance modeling of Hadoop jobs (either online or offline),
traffic at the edges of a data center by examining SNMP             and adaptive scheduling in dynamic conditions.
traces from routers.                                                   Another related approach argues for making MapReduce
                                                                   frameworks energy-aware [50]. The essential idea of this ap-
6.6 Data security                                                  proach is to turn Hadoop node into sleep mode when it has
Data security is another important research topic in cloud         finished its job while waiting for new assignments. To do so,
computing. Since service providers typically do not have ac-       both Hadoop and HDFS must be made energy-aware. Fur-
cess to the physical security system of data centers, they         thermore, there is often a trade-off between performance and
must rely on the infrastructure provider to achieve full           energy-awareness. Depending on the objective, finding a de-
data security. Even for a virtual private cloud, the service       sirable trade-off point is still an unexplored research topic.
provider can only specify the security setting remotely, with-
                                                                   6.8 Storage technologies and data management
out knowing whether it is fully implemented. The infrastruc-
ture provider, in this context, must achieve the following
                                                                   Software frameworks such as MapReduce and its various
objectives: (1) confidentiality, for secure data access and
                                                                   implementations such as Hadoop and Dryad are designed
transfer, and (2) auditability, for attesting whether secu-
                                                                   for distributed processing of data-intensive tasks. As men-
rity setting of applications has been tampered or not. Con-
                                                                   tioned previously, these frameworks typically operate on
fidentiality is usually achieved using cryptographic proto-
                                                                   Internet-scale file systems such as GFS and HDFS. These
cols, whereas auditability can be achieved using remote at-
                                                                   file systems are different from traditional distributed file sys-
testation techniques. Remote attestation typically requires a
trusted platform module (TPM) to generate non-forgeable            tems in their storage structure, access pattern and application
system summary (i.e. system state encrypted using TPM’s            programming interface. In particular, they do not implement
private key) as the proof of system security. However, in a        the standard POSIX interface, and therefore introduce com-
virtualized environment like the clouds, VMs can dynami-           patibility issues with legacy file systems and applications.
cally migrate from one location to another, hence directly         Several research efforts have studied this problem [4, 40].
using remote attestation is not sufficient. In this case, it is     For instance, the work in [4] proposed a method for sup-
critical to build trust mechanisms at every architectural layer    porting the MapReduce framework using cluster file sys-
of the cloud. Firstly, the hardware layer must be trusted          tems such as IBM’s GPFS. Patil et al. [40] proposed new
using hardware TPM. Secondly, the virtualization platform          API primitives for scalable and concurrent data access.
must be trusted using secure virtual machine monitors [43].
VM migration should only be allowed if both source and             6.9 Novel cloud architectures
destination servers are trusted. Recent work has been de-
                                                                   Currently, most of the commercial clouds are implemented
voted to designing efficient protocols for trust establishment
                                                                   in large data centers and operated in a centralized fashion.
and management [31, 43].
                                                                   Although this design achieves economy-of-scale and high
6.7 Software frameworks                                            manageability, it also comes with its limitations such high
                                                                   energy expense and high initial investment for construct-
Cloud computing provides a compelling platform for host-           ing data centers. Recent work [12, 48] suggests that small-
ing large-scale data-intensive applications. Typically, these      size data centers can be more advantageous than big data
J Internet Serv Appl (2010) 1: 7–18                                                                                                      17

centers in many cases: a small data center does not con-              4. Ananthanarayanan R, Gupta K et al (2009) Cloud analytics: do we
sume so much power, hence it does not require a power-                   really need to reinvent the storage stack? In: Proc of HotCloud
                                                                      5. Armbrust M et al (2009) Above the clouds: a Berkeley view of
ful and yet expensive cooling system; small data centers are
                                                                         cloud computing. UC Berkeley Technical Report
cheaper to build and better geographically distributed than           6. Berners-Lee T, Fielding R, Masinter L (2005) RFC 3986: uniform
large data centers. Geo-diversity is often desirable for re-             resource identifier (URI): generic syntax, January 2005
sponse time-critical services such as content delivery and            7. Bodik P et al (2009) Statistical machine learning makes automatic
                                                                         control practical for Internet datacenters. In: Proc HotCloud
interactive gaming. For example, Valancius et al. [48] stud-
                                                                      8. Brooks D et al (2000) Power-aware microarchitecture: design
ied the feasibility of hosting video-streaming services using            and modeling challenges for the next-generation microprocessors,
application gateways (a.k.a. nano-data centers).                         IEEE Micro
   Another related research trend is on using voluntary re-           9. Chandra A et al (2009) Nebulas: using distributed voluntary re-
                                                                         sources to build clouds. In: Proc of HotCloud
sources (i.e. resources donated by end-users) for hosting
                                                                     10. Chang F, Dean J et al (2006) Bigtable: a distributed storage system
cloud applications [9]. Clouds built using voluntary re-                 for structured data. In: Proc of OSDI
sources, or a mixture of voluntary and dedicated resources           11. Chekuri C, Khanna S (2004) On multi-dimensional packing prob-
are much cheaper to operate and more suitable for non-profit              lems. SIAM J Comput 33(4):837–851
applications such as scientific computing. However, this ar-          12. Church K et al (2008) On delivering embarrassingly distributed
                                                                         cloud services. In: Proc of HotNets
chitecture also imposes challenges such managing heteroge-           13. Clark C, Fraser K, Hand S, Hansen JG, Jul E, Limpach C, Pratt I,
neous resources and frequent churn events. Also, devising                Warfield A (2005) Live migration of virtual machines. In: Proc of
incentive schemes for such architectures is an open research             NSDI
problem.                                                             14. Cloud Computing on Wikipedia, en.wikipedia.org/wiki/
                                                                         Cloudcomputing, 20 Dec 2009
                                                                     15. Cloud Hosting, CLoud Computing and Hybrid Infrastructure from
                                                                         GoGrid, http://www.gogrid.com
7 Conclusion                                                         16. Dean J, Ghemawat S (2004) MapReduce: simplified data process-
                                                                         ing on large clusters. In: Proc of OSDI
                                                                     17. Dedicated Server, Managed Hosting, Web Hosting by Rackspace
Cloud computing has recently emerged as a compelling par-                Hosting, http://www.rackspace.com
adigm for managing and delivering services over the Inter-           18. FlexiScale Cloud Comp and Hosting, www.flexiscale.com
net. The rise of cloud computing is rapidly changing the             19. Ghemawat S, Gobioff H, Leung S-T (2003) The Google file sys-
landscape of information technology, and ultimately turning              tem. In: Proc of SOSP, October 2003
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to realize its full potential. Many key challenges in this               structure for data centers. In: Proc SIGCOMM
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fore, we believe there is still tremendous opportunity for re-       25. Hadoop MapReduce, hadoop.apache.org/mapreduce
                                                                     26. Hamilton J (2009) Cooperative expendable micro-slice servers
searchers to make groundbreaking contributions in this field,
                                                                         (CEMS): low cost, low power servers for Internet-scale services
and bring significant impact to their development in the in-              In: Proc of CIDR
dustry.                                                              27. IEEE P802.3az Energy Efficient Ethernet Task Force, www.
   In this paper, we have surveyed the state-of-the-art of               ieee802.org/3/az
                                                                     28. Kalyvianaki E et al (2009) Self-adaptive and self-configured CPU
cloud computing, covering its essential concepts, architec-
                                                                         resource provisioning for virtualized servers using Kalman filters.
tural designs, prominent characteristics, key technologies as            In: Proc of international conference on autonomic computing
well as research directions. As the development of cloud             29. Kambatla K et al (2009) Towards optimizing Hadoop provisioning
computing technology is still at an early stage, we hope our             in the cloud. In: Proc of HotCloud
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  • 1. J Internet Serv Appl (2010) 1: 7–18 DOI 10.1007/s13174-010-0007-6 O R I G I N A L PA P E R S Cloud computing: state-of-the-art and research challenges Qi Zhang · Lu Cheng · Raouf Boutaba Received: 8 January 2010 / Accepted: 25 February 2010 / Published online: 20 April 2010 © The Brazilian Computer Society 2010 Abstract Cloud computing has recently emerged as a new called cloud computing, in which resources (e.g., CPU and paradigm for hosting and delivering services over the Inter- storage) are provided as general utilities that can be leased net. Cloud computing is attractive to business owners as it and released by users through the Internet in an on-demand eliminates the requirement for users to plan ahead for pro- fashion. In a cloud computing environment, the traditional visioning, and allows enterprises to start from the small and role of service provider is divided into two: the infrastruc- increase resources only when there is a rise in service de- ture providers who manage cloud platforms and lease re- mand. However, despite the fact that cloud computing offers sources according to a usage-based pricing model, and ser- huge opportunities to the IT industry, the development of vice providers, who rent resources from one or many in- cloud computing technology is currently at its infancy, with frastructure providers to serve the end users. The emer- many issues still to be addressed. In this paper, we present a gence of cloud computing has made a tremendous impact survey of cloud computing, highlighting its key concepts, on the Information Technology (IT) industry over the past architectural principles, state-of-the-art implementation as few years, where large companies such as Google, Ama- well as research challenges. The aim of this paper is to pro- zon and Microsoft strive to provide more powerful, reliable vide a better understanding of the design challenges of cloud and cost-efficient cloud platforms, and business enterprises computing and identify important research directions in this seek to reshape their business models to gain benefit from increasingly important area. this new paradigm. Indeed, cloud computing provides sev- eral compelling features that make it attractive to business Keywords Cloud computing · Data centers · Virtualization owners, as shown below. No up-front investment: Cloud computing uses a pay-as- you-go pricing model. A service provider does not need to 1 Introduction invest in the infrastructure to start gaining benefit from cloud computing. It simply rents resources from the cloud accord- With the rapid development of processing and storage tech- ing to its own needs and pay for the usage. nologies and the success of the Internet, computing re- Lowering operating cost: Resources in a cloud environ- sources have become cheaper, more powerful and more ment can be rapidly allocated and de-allocated on demand. ubiquitously available than ever before. This technological Hence, a service provider no longer needs to provision ca- trend has enabled the realization of a new computing model pacities according to the peak load. This provides huge sav- ings since resources can be released to save on operating costs when service demand is low. Q. Zhang · L. Cheng · R. Boutaba ( ) University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1 Highly scalable: Infrastructure providers pool large e-mail: rboutaba@cs.uwaterloo.ca amount of resources from data centers and make them easily Q. Zhang accessible. A service provider can easily expand its service e-mail: q8zhang@cs.uwaterloo.ca to large scales in order to handle rapid increase in service L. Cheng demands (e.g., flash-crowd effect). This model is sometimes e-mail: l32cheng@cs.uwaterloo.ca called surge computing [5].
  • 2. 8 J Internet Serv Appl (2010) 1: 7–18 Easy access: Services hosted in the cloud are generally has generated not only market hypes, but also a fair amount web-based. Therefore, they are easily accessible through a of skepticism and confusion. For this reason, recently there variety of devices with Internet connections. These devices has been work on standardizing the definition of cloud com- not only include desktop and laptop computers, but also cell puting. As an example, the work in [49] compared over 20 phones and PDAs. different definitions from a variety of sources to confirm a Reducing business risks and maintenance expenses: By standard definition. In this paper, we adopt the definition outsourcing the service infrastructure to the clouds, a service of cloud computing provided by The National Institute of provider shifts its business risks (such as hardware failures) Standards and Technology (NIST) [36], as it covers, in our to infrastructure providers, who often have better expertise opinion, all the essential aspects of cloud computing: and are better equipped for managing these risks. In addi- tion, a service provider can cut down the hardware mainte- NIST definition of cloud computing Cloud computing is a nance and the staff training costs. model for enabling convenient, on-demand network access However, although cloud computing has shown consid- to a shared pool of configurable computing resources (e.g., erable opportunities to the IT industry, it also brings many networks, servers, storage, applications, and services) that unique challenges that need to be carefully addressed. In this can be rapidly provisioned and released with minimal man- paper, we present a survey of cloud computing, highlighting agement effort or service provider interaction. its key concepts, architectural principles, state-of-the-art im- plementations as well as research challenges. Our aim is to The main reason for the existence of different percep- provide a better understanding of the design challenges of tions of cloud computing is that cloud computing, unlike cloud computing and identify important research directions other technical terms, is not a new technology, but rather in this fascinating topic. a new operations model that brings together a set of ex- The remainder of this paper is organized as follows. In isting technologies to run business in a different way. In- Sect. 2 we provide an overview of cloud computing and deed, most of the technologies used by cloud computing, compare it with other related technologies. In Sect. 3, we such as virtualization and utility-based pricing, are not new. describe the architecture of cloud computing and present Instead, cloud computing leverages these existing technolo- gies to meet the technological and economic requirements its design principles. The key features and characteristics of of today’s demand for information technology. cloud computing are detailed in Sect. 4. Section 5 surveys the commercial products as well as the current technologies 2.2 Related technologies used for cloud computing. In Sect. 6, we summarize the cur- rent research topics in cloud computing. Finally, the paper Cloud computing is often compared to the following tech- concludes in Sect. 7. nologies, each of which shares certain aspects with cloud computing: Grid Computing: Grid computing is a distributed com- 2 Overview of cloud computing puting paradigm that coordinates networked resources to achieve a common computational objective. The develop- This section presents a general overview of cloud comput- ment of Grid computing was originally driven by scien- ing, including its definition and a comparison with related tific applications which are usually computation-intensive. concepts. Cloud computing is similar to Grid computing in that it also employs distributed resources to achieve application-level 2.1 Definitions objectives. However, cloud computing takes one step further by leveraging virtualization technologies at multiple levels The main idea behind cloud computing is not a new one. (hardware and application platform) to realize resource shar- John McCarthy in the 1960s already envisioned that com- ing and dynamic resource provisioning. puting facilities will be provided to the general public like Utility Computing: Utility computing represents the a utility [39]. The term “cloud” has also been used in vari- model of providing resources on-demand and charging cus- ous contexts such as describing large ATM networks in the tomers based on usage rather than a flat rate. Cloud comput- 1990s. However, it was after Google’s CEO Eric Schmidt ing can be perceived as a realization of utility computing. It used the word to describe the business model of provid- adopts a utility-based pricing scheme entirely for economic ing services across the Internet in 2006, that the term re- reasons. With on-demand resource provisioning and utility- ally started to gain popularity. Since then, the term cloud based pricing, service providers can truly maximize resource computing has been used mainly as a marketing term in a utilization and minimize their operating costs. variety of contexts to represent many different ideas. Cer- Virtualization: Virtualization is a technology that ab- tainly, the lack of a standard definition of cloud computing stracts away the details of physical hardware and provides
  • 3. J Internet Serv Appl (2010) 1: 7–18 9 virtualized resources for high-level applications. A virtual- The hardware layer: This layer is responsible for man- ized server is commonly called a virtual machine (VM). Vir- aging the physical resources of the cloud, including phys- tualization forms the foundation of cloud computing, as it ical servers, routers, switches, power and cooling systems. provides the capability of pooling computing resources from In practice, the hardware layer is typically implemented clusters of servers and dynamically assigning or reassigning in data centers. A data center usually contains thousands virtual resources to applications on-demand. of servers that are organized in racks and interconnected Autonomic Computing: Originally coined by IBM in through switches, routers or other fabrics. Typical issues 2001, autonomic computing aims at building computing sys- at hardware layer include hardware configuration, fault- tems capable of self-management, i.e. reacting to internal tolerance, traffic management, power and cooling resource and external observations without human intervention. The management. goal of autonomic computing is to overcome the manage- The infrastructure layer: Also known as the virtualiza- ment complexity of today’s computer systems. Although tion layer, the infrastructure layer creates a pool of storage cloud computing exhibits certain autonomic features such and computing resources by partitioning the physical re- as automatic resource provisioning, its objective is to lower sources using virtualization technologies such as Xen [55], the resource cost rather than to reduce system complexity. KVM [30] and VMware [52]. The infrastructure layer is an In summary, cloud computing leverages virtualization essential component of cloud computing, since many key technology to achieve the goal of providing computing re- features, such as dynamic resource assignment, are only sources as a utility. It shares certain aspects with grid com- made available through virtualization technologies. puting and autonomic computing but differs from them in The platform layer: Built on top of the infrastructure other aspects. Therefore, it offers unique benefits and im- layer, the platform layer consists of operating systems and poses distinctive challenges to meet its requirements. application frameworks. The purpose of the platform layer is to minimize the burden of deploying applications directly into VM containers. For example, Google App Engine oper- ates at the platform layer to provide API support for imple- 3 Cloud computing architecture menting storage, database and business logic of typical web applications. This section describes the architectural, business and various The application layer: At the highest level of the hierar- operation models of cloud computing. chy, the application layer consists of the actual cloud appli- cations. Different from traditional applications, cloud appli- 3.1 A layered model of cloud computing cations can leverage the automatic-scaling feature to achieve better performance, availability and lower operating cost. Generally speaking, the architecture of a cloud comput- Compared to traditional service hosting environments ing environment can be divided into 4 layers: the hard- such as dedicated server farms, the architecture of cloud ware/datacenter layer, the infrastructure layer, the platform computing is more modular. Each layer is loosely coupled layer and the application layer, as shown in Fig. 1. We de- with the layers above and below, allowing each layer to scribe each of them in detail: evolve separately. This is similar to the design of the OSI Fig. 1 Cloud computing architecture
  • 4. 10 J Internet Serv Appl (2010) 1: 7–18 model for network protocols. The architectural modularity 3.3 Types of clouds allows cloud computing to support a wide range of applica- tion requirements while reducing management and mainte- There are many issues to consider when moving an enter- nance overhead. prise application to the cloud environment. For example, some service providers are mostly interested in lowering op- 3.2 Business model eration cost, while others may prefer high reliability and se- curity. Accordingly, there are different types of clouds, each with its own benefits and drawbacks: Cloud computing employs a service-driven business model. Public clouds: A cloud in which service providers of- In other words, hardware and platform-level resources are fer their resources as services to the general public. Pub- provided as services on an on-demand basis. Conceptually, lic clouds offer several key benefits to service providers, in- every layer of the architecture described in the previous sec- cluding no initial capital investment on infrastructure and tion can be implemented as a service to the layer above. shifting of risks to infrastructure providers. However, pub- Conversely, every layer can be perceived as a customer of lic clouds lack fine-grained control over data, network and the layer below. However, in practice, clouds offer services security settings, which hampers their effectiveness in many that can be grouped into three categories: software as a ser- business scenarios. vice (SaaS), platform as a service (PaaS), and infrastructure Private clouds: Also known as internal clouds, private as a service (IaaS). clouds are designed for exclusive use by a single organiza- 1. Infrastructure as a Service: IaaS refers to on-demand tion. A private cloud may be built and managed by the orga- nization or by external providers. A private cloud offers the provisioning of infrastructural resources, usually in terms highest degree of control over performance, reliability and of VMs. The cloud owner who offers IaaS is called an security. However, they are often criticized for being simi- IaaS provider. Examples of IaaS providers include Ama- lar to traditional proprietary server farms and do not provide zon EC2 [2], GoGrid [15] and Flexiscale [18]. benefits such as no up-front capital costs. 2. Platform as a Service: PaaS refers to providing platform Hybrid clouds: A hybrid cloud is a combination of public layer resources, including operating system support and and private cloud models that tries to address the limitations software development frameworks. Examples of PaaS of each approach. In a hybrid cloud, part of the service in- providers include Google App Engine [20], Microsoft frastructure runs in private clouds while the remaining part Windows Azure [53] and Force.com [41]. runs in public clouds. Hybrid clouds offer more flexibility 3. Software as a Service: SaaS refers to providing on- than both public and private clouds. Specifically, they pro- demand applications over the Internet. Examples of SaaS vide tighter control and security over application data com- providers include Salesforce.com [41], Rackspace [17] pared to public clouds, while still facilitating on-demand and SAP Business ByDesign [44]. service expansion and contraction. On the down side, de- The business model of cloud computing is depicted by signing a hybrid cloud requires carefully determining the Fig. 2. According to the layered architecture of cloud com- best split between public and private cloud components. puting, it is entirely possible that a PaaS provider runs its Virtual Private Cloud: An alternative solution to address- cloud on top of an IaaS provider’s cloud. However, in the ing the limitations of both public and private clouds is called current practice, IaaS and PaaS providers are often parts of Virtual Private Cloud (VPC). A VPC is essentially a plat- the same organization (e.g., Google and Salesforce). This is form running on top of public clouds. The main difference is that a VPC leverages virtual private network (VPN) technol- why PaaS and IaaS providers are often called the infrastruc- ogy that allows service providers to design their own topol- ture providers or cloud providers [5]. ogy and security settings such as firewall rules. VPC is es- sentially a more holistic design since it not only virtualizes servers and applications, but also the underlying commu- nication network as well. Additionally, for most companies, VPC provides seamless transition from a proprietary service infrastructure to a cloud-based infrastructure, owing to the virtualized network layer. For most service providers, selecting the right cloud model is dependent on the business scenario. For exam- ple, computation-intensive scientific applications are best deployed on public clouds for cost-effectiveness. Arguably, Fig. 2 Business model of cloud computing certain types of clouds will be more popular than others.
  • 5. J Internet Serv Appl (2010) 1: 7–18 11 In particular, it was predicted that hybrid clouds will be the Self-organizing: Since resources can be allocated or de- dominant type for most organizations [14]. However, vir- allocated on-demand, service providers are empowered to tual private clouds have started to gain more popularity since manage their resource consumption according to their own their inception in 2009. needs. Furthermore, the automated resource management feature yields high agility that enables service providers to respond quickly to rapid changes in service demand such as 4 Cloud computing characteristics the flash crowd effect. Utility-based pricing: Cloud computing employs a pay- Cloud computing provides several salient features that are per-use pricing model. The exact pricing scheme may vary different from traditional service computing, which we sum- from service to service. For example, a SaaS provider may marize below: rent a virtual machine from an IaaS provider on a per-hour Multi-tenancy: In a cloud environment, services owned basis. On the other hand, a SaaS provider that provides by multiple providers are co-located in a single data center. on-demand customer relationship management (CRM) may The performance and management issues of these services charge its customers based on the number of clients it serves are shared among service providers and the infrastructure (e.g., Salesforce). Utility-based pricing lowers service oper- provider. The layered architecture of cloud computing pro- ating cost as it charges customers on a per-use basis. How- vides a natural division of responsibilities: the owner of each ever, it also introduces complexities in controlling the oper- layer only needs to focus on the specific objectives associ- ating cost. In this perspective, companies like VKernel [51] ated with this layer. However, multi-tenancy also introduces provide software to help cloud customers understand, ana- difficulties in understanding and managing the interactions lyze and cut down the unnecessary cost on resource con- among various stakeholders. sumption. Shared resource pooling: The infrastructure provider of- fers a pool of computing resources that can be dynamically assigned to multiple resource consumers. Such dynamic re- 5 State-of-the-art source assignment capability provides much flexibility to in- frastructure providers for managing their own resource us- In this section, we present the state-of-the-art implementa- age and operating costs. For instance, an IaaS provider can tions of cloud computing. We first describe the key technolo- leverage VM migration technology to attain a high degree of server consolidation, hence maximizing resource utiliza- gies currently used for cloud computing. Then, we survey tion while minimizing cost such as power consumption and the popular cloud computing products. cooling. Geo-distribution and ubiquitous network access: Clouds 5.1 Cloud computing technologies are generally accessible through the Internet and use the Internet as a service delivery network. Hence any device This section provides a review of technologies used in cloud with Internet connectivity, be it a mobile phone, a PDA or computing environments. a laptop, is able to access cloud services. Additionally, to achieve high network performance and localization, many 5.1.1 Architectural design of data centers of today’s clouds consist of data centers located at many locations around the globe. A service provider can easily A data center, which is home to the computation power and leverage geo-diversity to achieve maximum service utility. storage, is central to cloud computing and contains thou- Service oriented: As mentioned previously, cloud com- sands of devices like servers, switches and routers. Proper puting adopts a service-driven operating model. Hence it planning of this network architecture is critical, as it will places a strong emphasis on service management. In a cloud, heavily influence applications performance and throughput each IaaS, PaaS and SaaS provider offers its service accord- in such a distributed computing environment. Further, scala- ing to the Service Level Agreement (SLA) negotiated with bility and resiliency features need to be carefully considered. its customers. SLA assurance is therefore a critical objective Currently, a layered approach is the basic foundation of of every provider. the network architecture design, which has been tested in Dynamic resource provisioning: One of the key features some of the largest deployed data centers. The basic layers of cloud computing is that computing resources can be ob- of a data center consist of the core, aggregation, and access tained and released on the fly. Compared to the traditional layers, as shown in Fig. 3. The access layer is where the model that provisions resources according to peak demand, servers in racks physically connect to the network. There dynamic resource provisioning allows service providers to are typically 20 to 40 servers per rack, each connected to an acquire resources based on the current demand, which can access switch with a 1 Gbps link. Access switches usually considerably lower the operating cost. connect to two aggregation switches for redundancy with
  • 6. 12 J Internet Serv Appl (2010) 1: 7–18 Scalability: The network infrastructure must be able to scale to a large number of servers and allow for incremental expansion. Backward compatibility: The network infrastructure should be backward compatible with switches and routers running Ethernet and IP. Because existing data centers have commonly leveraged commodity Ethernet and IP based de- vices, they should also be used in the new architecture with- out major modifications. Another area of rapid innovation in the industry is the de- sign and deployment of shipping-container based, modular data center (MDC). In an MDC, normally up to a few thou- sands of servers, are interconnected via switches to form the network infrastructure. Highly interactive applications, which are sensitive to response time, are suitable for geo- Fig. 3 Basic layered design of data center network infrastructure diverse MDC placed close to major population areas. The MDC also helps with redundancy because not all areas are likely to lose power, experience an earthquake, or suffer ri- 10 Gbps links. The aggregation layer usually provides im- ots at the same time. Rather than the three-layered approach portant functions, such as domain service, location service, discussed above, Guo et al. [22, 23] proposed server-centric, server load balancing, and more. The core layer provides recursively defined network structures of MDC. connectivity to multiple aggregation switches and provides a resilient routed fabric with no single point of failure. The 5.1.2 Distributed file system over clouds core routers manage traffic into and out of the data center. A popular practice is to leverage commodity Ethernet Google File System (GFS) [19] is a proprietary distributed switches and routers to build the network infrastructure. In file system developed by Google and specially designed to different business solutions, the layered network infrastruc- provide efficient, reliable access to data using large clusters ture can be elaborated to meet specific business challenges. of commodity servers. Files are divided into chunks of 64 Basically, the design of a data center network architecture megabytes, and are usually appended to or read and only should meet the following objectives [1, 21–23, 35]: extremely rarely overwritten or shrunk. Compared with tra- Uniform high capacity: The maximum rate of a server- ditional file systems, GFS is designed and optimized to run to-server traffic flow should be limited only by the available on data centers to provide extremely high data throughputs, capacity on the network-interface cards of the sending and low latency and survive individual server failures. receiving servers, and assigning servers to a service should Inspired by GFS, the open source Hadoop Distributed be independent of the network topology. It should be possi- File System (HDFS) [24] stores large files across multi- ble for an arbitrary host in the data center to communicate ple machines. It achieves reliability by replicating the data with any other host in the network at the full bandwidth of across multiple servers. Similarly to GFS, data is stored on multiple geo-diverse nodes. The file system is built from a its local network interface. cluster of data nodes, each of which serves blocks of data Free VM migration: Virtualization allows the entire VM over the network using a block protocol specific to HDFS. state to be transmitted across the network to migrate a VM Data is also provided over HTTP, allowing access to all con- from one physical machine to another. A cloud comput- tent from a web browser or other types of clients. Data nodes ing hosting service may migrate VMs for statistical multi- can talk to each other to rebalance data distribution, to move plexing or dynamically changing communication patterns copies around, and to keep the replication of data high. to achieve high bandwidth for tightly coupled hosts or to achieve variable heat distribution and power availability in 5.1.3 Distributed application framework over clouds the data center. The communication topology should be de- signed so as to support rapid virtual machine migration. HTTP-based applications usually conform to some web ap- Resiliency: Failures will be common at scale. The net- plication framework such as Java EE. In modern data center work infrastructure must be fault-tolerant against various environments, clusters of servers are also used for computa- types of server failures, link outages, or server-rack failures. tion and data-intensive jobs such as financial trend analysis, Existing unicast and multicast communications should not or film animation. be affected to the extent allowed by the underlying physical MapReduce [16] is a software framework introduced by connectivity. Google to support distributed computing on large data sets
  • 7. J Internet Serv Appl (2010) 1: 7–18 13 on clusters of computers. MapReduce consists of one Mas- data as “objects” that are grouped in “buckets.” Each object ter, to which client applications submit MapReduce jobs. contains from 1 byte to 5 gigabytes of data. Object names The Master pushes work out to available task nodes in the are essentially URI [6] pathnames. Buckets must be explic- data center, striving to keep the tasks as close to the data itly created before they can be used. A bucket can be stored as possible. The Master knows which node contains the in one of several Regions. Users can choose a Region to opti- data, and which other hosts are nearby. If the task cannot mize latency, minimize costs, or address regulatory require- be hosted on the node where the data is stored, priority is ments. given to nodes in the same rack. In this way, network traffic Amazon Virtual Private Cloud (VPC) is a secure and on the main backbone is reduced, which also helps to im- seamless bridge between a company’s existing IT infrastruc- prove throughput, as the backbone is usually the bottleneck. ture and the AWS cloud. Amazon VPC enables enterprises If a task fails or times out, it is rescheduled. If the Master to connect their existing infrastructure to a set of isolated fails, all ongoing tasks are lost. The Master records what it AWS compute resources via a Virtual Private Network is up to in the filesystem. When it starts up, it looks for any (VPN) connection, and to extend their existing management such data, so that it can restart work from where it left off. capabilities such as security services, firewalls, and intrusion The open source Hadoop MapReduce project [25] is in- detection systems to include their AWS resources. spired by Google’s work. Currently, many organizations are For cloud users, Amazon CloudWatch is a useful man- using Hadoop MapReduce to run large data-intensive com- agement tool which collects raw data from partnered AWS putations. services such as Amazon EC2 and then processes the in- formation into readable, near real-time metrics. The metrics 5.2 Commercial products about EC2 include, for example, CPU utilization, network in/out bytes, disk read/write operations, etc. In this section, we provide a survey of some of the dominant cloud computing products. 5.2.2 Microsoft Windows Azure platform 5.2.1 Amazon EC2 Microsoft’s Windows Azure platform [53] consists of three Amazon Web Services (AWS) [3] is a set of cloud services, components and each of them provides a specific set of ser- providing cloud-based computation, storage and other func- vices to cloud users. Windows Azure provides a Windows- tionality that enable organizations and individuals to deploy based environment for running applications and storing data applications and services on an on-demand basis and at com- on servers in data centers; SQL Azure provides data services modity prices. Amazon Web Services’ offerings are acces- in the cloud based on SQL Server; and .NET Services offer sible over HTTP, using REST and SOAP protocols. distributed infrastructure services to cloud-based and local Amazon Elastic Compute Cloud (Amazon EC2) enables applications. Windows Azure platform can be used both by cloud users to launch and manage server instances in data applications running in the cloud and by applications run- centers using APIs or available tools and utilities. EC2 in- ning on local systems. stances are virtual machines running on top of the Xen virtu- Windows Azure also supports applications built on the alization engine [55]. After creating and starting an instance, .NET Framework and other ordinary languages supported in users can upload software and make changes to it. When Windows systems, like C#, Visual Basic, C++, and others. changes are finished, they can be bundled as a new machine Windows Azure supports general-purpose programs, rather image. An identical copy can then be launched at any time. than a single class of computing. Developers can create web Users have nearly full control of the entire software stack applications using technologies such as ASP.NET and Win- on the EC2 instances that look like hardware to them. On dows Communication Foundation (WCF), applications that the other hand, this feature makes it inherently difficult for run as independent background processes, or applications Amazon to offer automatic scaling of resources. that combine the two. Windows Azure allows storing data EC2 provides the ability to place instances in multiple lo- in blobs, tables, and queues, all accessed in a RESTful style cations. EC2 locations are composed of Regions and Avail- via HTTP or HTTPS. ability Zones. Regions consist of one or more Availability SQL Azure components are SQL Azure Database and Zones, are geographically dispersed. Availability Zones are “Huron” Data Sync. SQL Azure Database is built on Mi- distinct locations that are engineered to be insulated from crosoft SQL Server, providing a database management sys- failures in other Availability Zones and provide inexpensive, tem (DBMS) in the cloud. The data can be accessed using low latency network connectivity to other Availability Zones ADO.NET and other Windows data access interfaces. Users in the same Region. can also use on-premises software to work with this cloud- EC2 machine images are stored in and retrieved from based information. “Huron” Data Sync synchronizes rela- Amazon Simple Storage Service (Amazon S3). S3 stores tional data across various on-premises DBMSs.
  • 8. 14 J Internet Serv Appl (2010) 1: 7–18 Table 1 A comparison of representative commercial products Cloud Provider Amazon EC2 Windows Azure Google App Engine Classes of Utility Computing Infrastructure service Platform service Platform service Target Applications General-purpose applications General-purpose Windows Traditional web applications applications with supported framework Computation OS Level on a Xen Virtual Microsoft Common Language Predefined web application Machine Runtime (CLR) VM; Predefined frameworks roles of app. instances Storage Elastic Block Store; Amazon Azure storage service and SQL BigTable and MegaStore Simple Storage Service (S3); Data Services Amazon SimpleDB Auto Scaling Automatically changing the Automatic scaling based on Automatic Scaling which is number of instances based on application roles and a transparent to users parameters that users specify configuration file specified by users The .NET Services facilitate the creation of distributed and management of the resources. Users can choose one applications. The Access Control component provides a type or combinations of several types of cloud offerings to cloud-based implementation of single identity verification satisfy specific business requirements. across applications and companies. The Service Bus helps an application expose web services endpoints that can be accessed by other applications, whether on-premises or in 6 Research challenges the cloud. Each exposed endpoint is assigned a URI, which clients can use to locate and access a service. Although cloud computing has been widely adopted by the All of the physical resources, VMs and applications in industry, the research on cloud computing is still at an early the data center are monitored by software called the fabric stage. Many existing issues have not been fully addressed, controller. With each application, the users upload a config- while new challenges keep emerging from industry applica- uration file that provides an XML-based description of what tions. In this section, we summarize some of the challenging the application needs. Based on this file, the fabric controller research issues in cloud computing. decides where new applications should run, choosing phys- ical servers to optimize hardware utilization. 6.1 Automated service provisioning 5.2.3 Google App Engine One of the key features of cloud computing is the capabil- ity of acquiring and releasing resources on-demand. The ob- Google App Engine [20] is a platform for traditional web jective of a service provider in this case is to allocate and applications in Google-managed data centers. Currently, the de-allocate resources from the cloud to satisfy its service supported programming languages are Python and Java. level objectives (SLOs), while minimizing its operational Web frameworks that run on the Google App Engine include cost. However, it is not obvious how a service provider can Django, CherryPy, Pylons, and web2py, as well as a custom achieve this objective. In particular, it is not easy to de- Google-written web application framework similar to JSP termine how to map SLOs such as QoS requirements to or ASP.NET. Google handles deploying code to a cluster, low-level resource requirement such as CPU and memory monitoring, failover, and launching application instances as requirements. Furthermore, to achieve high agility and re- necessary. Current APIs support features such as storing and spond to rapid demand fluctuations such as in flash crowd retrieving data from a BigTable [10] non-relational database, effect, the resource provisioning decisions must be made on- making HTTP requests and caching. Developers have read- line. only access to the filesystem on App Engine. Automated service provisioning is not a new problem. Table 1 summarizes the three examples of popular cloud Dynamic resource provisioning for Internet applications has offerings in terms of the classes of utility computing, tar- been studied extensively in the past [47, 57]. These ap- get types of application, and more importantly their models proaches typically involve: (1) Constructing an application of computation, storage and auto-scaling. Apparently, these performance model that predicts the number of application cloud offerings are based on different levels of abstraction instances required to handle demand at each particular level,
  • 9. J Internet Serv Appl (2010) 1: 7–18 15 in order to satisfy QoS requirements; (2) Periodically pre- communication requirements, have also been considered re- dicting future demand and determining resource require- cently [34]. ments using the performance model; and (3) Automatically However, server consolidation activities should not hurt allocating resources using the predicted resource require- application performance. It is known that the resource usage ments. Application performance model can be constructed (also known as the footprint [45]) of individual VMs may using various techniques, including Queuing theory [47], vary over time [54]. For server resources that are shared Control theory [28] and Statistical Machine Learning [7]. among VMs, such as bandwidth, memory cache and disk Additionally, there is a distinction between proactive I/O, maximally consolidating a server may result in re- and reactive resource control. The proactive approach uses source congestion when a VM changes its footprint on the predicted demand to periodically allocate resources before server [38]. Hence, it is sometimes important to observe they are needed. The reactive approach reacts to immedi- the fluctuations of VM footprints and use this information ate demand fluctuations before periodic demand prediction for effective server consolidation. Finally, the system must is available. Both approaches are important and necessary quickly react to resource congestions when they occur [54]. for effective resource control in dynamic operating environ- ments. 6.4 Energy management 6.2 Virtual machine migration Improving energy efficiency is another major issue in cloud computing. It has been estimated that the cost of powering Virtualization can provide significant benefits in cloud com- and cooling accounts for 53% of the total operational expen- puting by enabling virtual machine migration to balance diture of data centers [26]. In 2006, data centers in the US load across the data center. In addition, virtual machine mi- consumed more than 1.5% of the total energy generated in gration enables robust and highly responsive provisioning in that year, and the percentage is projected to grow 18% an- data centers. nually [33]. Hence infrastructure providers are under enor- Virtual machine migration has evolved from process mous pressure to reduce energy consumption. The goal is migration techniques [37]. More recently, Xen [55] and not only to cut down energy cost in data centers, but also to VMWare [52] have implemented “live” migration of VMs meet government regulations and environmental standards. that involves extremely short downtimes ranging from tens Designing energy-efficient data centers has recently re- of milliseconds to a second. Clark et al. [13] pointed out that ceived considerable attention. This problem can be ap- migrating an entire OS and all of its applications as one unit proached from several directions. For example, energy- allows to avoid many of the difficulties faced by process- efficient hardware architecture that enables slowing down level migration approaches, and analyzed the benefits of live CPU speeds and turning off partial hardware components migration of VMs. [8] has become commonplace. Energy-aware job schedul- The major benefits of VM migration is to avoid hotspots; ing [50] and server consolidation [46] are two other ways to however, this is not straightforward. Currently, detecting reduce power consumption by turning off unused machines. workload hotspots and initiating a migration lacks the agility Recent research has also begun to study energy-efficient net- to respond to sudden workload changes. Moreover, the in- work protocols and infrastructures [27]. A key challenge in memory state should be transferred consistently and effi- all the above methods is to achieve a good trade-off between ciently, with integrated consideration of resources for appli- energy savings and application performance. In this respect, cations and physical servers. few researchers have recently started to investigate coordi- nated solutions for performance and power management in 6.3 Server consolidation a dynamic cloud environment [32]. Server consolidation is an effective approach to maximize 6.5 Traffic management and analysis resource utilization while minimizing energy consumption in a cloud computing environment. Live VM migration tech- Analysis of data traffic is important for today’s data cen- nology is often used to consolidate VMs residing on multi- ters. For example, many web applications rely on analysis ple under-utilized servers onto a single server, so that the of traffic data to optimize customer experiences. Network remaining servers can be set to an energy-saving state. The operators also need to know how traffic flows through the problem of optimally consolidating servers in a data center network in order to make many of the management and plan- is often formulated as a variant of the vector bin-packing ning decisions. problem [11], which is an NP-hard optimization problem. However, there are several challenges for existing traf- Various heuristics have been proposed for this problem fic measurement and analysis methods in Internet Service [33, 46]. Additionally, dependencies among VMs, such as Providers (ISPs) networks and enterprise to extend to data
  • 10. 16 J Internet Serv Appl (2010) 1: 7–18 centers. Firstly, the density of links is much higher than applications leverage MapReduce frameworks such as that in ISPs or enterprise networks, which makes the worst- Hadoop for scalable and fault-tolerant data processing. Re- case scenario for existing methods. Secondly, most existing cent work has shown that the performance and resource con- methods can compute traffic matrices between a few hun- sumption of a MapReduce job is highly dependent on the dreds end hosts, but even a modular data center can have type of the application [29, 42, 56]. For instance, Hadoop several thousand servers. Finally, existing methods usually tasks such as sort is I/O intensive, whereas grep requires assume some flow patterns that are reasonable in Internet significant CPU resources. Furthermore, the VM allocated and enterprises networks, but the applications deployed on to each Hadoop node may have heterogeneous character- data centers, such as MapReduce jobs, significantly change istics. For example, the bandwidth available to a VM is the traffic pattern. Further, there is tighter coupling in appli- dependent on other VMs collocated on the same server. cation’s use of network, computing, and storage resources, Hence, it is possible to optimize the performance and cost than what is seen in other settings. of a MapReduce application by carefully selecting its con- Currently, there is not much work on measurement and figuration parameter values [29] and designing more effi- analysis of data center traffic. Greenberg et al. [21] report cient scheduling algorithms [42, 56]. By mitigating the bot- data center traffic characteristics on flow sizes and concur- tleneck resources, execution time of applications can be rent flows, and use these to guide network infrastructure de- significantly improved. The key challenges include perfor- sign. Benson et al. [16] perform a complementary study of mance modeling of Hadoop jobs (either online or offline), traffic at the edges of a data center by examining SNMP and adaptive scheduling in dynamic conditions. traces from routers. Another related approach argues for making MapReduce frameworks energy-aware [50]. The essential idea of this ap- 6.6 Data security proach is to turn Hadoop node into sleep mode when it has Data security is another important research topic in cloud finished its job while waiting for new assignments. To do so, computing. Since service providers typically do not have ac- both Hadoop and HDFS must be made energy-aware. Fur- cess to the physical security system of data centers, they thermore, there is often a trade-off between performance and must rely on the infrastructure provider to achieve full energy-awareness. Depending on the objective, finding a de- data security. Even for a virtual private cloud, the service sirable trade-off point is still an unexplored research topic. provider can only specify the security setting remotely, with- 6.8 Storage technologies and data management out knowing whether it is fully implemented. The infrastruc- ture provider, in this context, must achieve the following Software frameworks such as MapReduce and its various objectives: (1) confidentiality, for secure data access and implementations such as Hadoop and Dryad are designed transfer, and (2) auditability, for attesting whether secu- for distributed processing of data-intensive tasks. As men- rity setting of applications has been tampered or not. Con- tioned previously, these frameworks typically operate on fidentiality is usually achieved using cryptographic proto- Internet-scale file systems such as GFS and HDFS. These cols, whereas auditability can be achieved using remote at- file systems are different from traditional distributed file sys- testation techniques. Remote attestation typically requires a trusted platform module (TPM) to generate non-forgeable tems in their storage structure, access pattern and application system summary (i.e. system state encrypted using TPM’s programming interface. In particular, they do not implement private key) as the proof of system security. However, in a the standard POSIX interface, and therefore introduce com- virtualized environment like the clouds, VMs can dynami- patibility issues with legacy file systems and applications. cally migrate from one location to another, hence directly Several research efforts have studied this problem [4, 40]. using remote attestation is not sufficient. In this case, it is For instance, the work in [4] proposed a method for sup- critical to build trust mechanisms at every architectural layer porting the MapReduce framework using cluster file sys- of the cloud. Firstly, the hardware layer must be trusted tems such as IBM’s GPFS. Patil et al. [40] proposed new using hardware TPM. Secondly, the virtualization platform API primitives for scalable and concurrent data access. must be trusted using secure virtual machine monitors [43]. VM migration should only be allowed if both source and 6.9 Novel cloud architectures destination servers are trusted. Recent work has been de- Currently, most of the commercial clouds are implemented voted to designing efficient protocols for trust establishment in large data centers and operated in a centralized fashion. and management [31, 43]. Although this design achieves economy-of-scale and high 6.7 Software frameworks manageability, it also comes with its limitations such high energy expense and high initial investment for construct- Cloud computing provides a compelling platform for host- ing data centers. Recent work [12, 48] suggests that small- ing large-scale data-intensive applications. Typically, these size data centers can be more advantageous than big data
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