J Internet Serv Appl (2010) 1 7–18DOI 10.1007s13174-010-00.docx

J Internet Serv Appl (2010) 1: 7–18
DOI 10.1007/s13174-010-0007-6
O R I G I NA 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
paradigm for hosting and delivering services over the Inter-
net. Cloud computing is attractive to business owners as it
eliminates the requirement for users to plan ahead for pro-
visioning, and allows enterprises to start from the small and
increase resources only when there is a rise in service de-
mand. However, despite the fact that cloud computing offers
huge opportunities to the IT industry, the development of
cloud computing technology is currently at its infancy, with
many issues still to be addressed. In this paper, we present a
survey of cloud computing, highlighting its key concepts,
architectural principles, state-of-the-art implementation as
well as research challenges. The aim of this paper is to pro-
vide a better understanding of the design challenges of cloud
computing and identify important research directions in this
increasingly important area.
Keywords Cloud computing · Data centers · Virtualization

1 Introduction
With the rapid development of processing and storage tech-
nologies and the success of the Internet, computing re-
sources have become cheaper, more powerful and more
ubiquitously available than ever before. This technological
trend has enabled the realization of a new computing model
Q. Zhang · L. Cheng · R. Boutaba (�)
University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1
e-mail: [email protected]
Q. Zhang
L. Cheng
called cloud computing, in which resources (e.g., CPU and
storage) are provided as general utilities that can be leased
and released by users through the Internet in an on-demand
fashion. In a cloud computing environment, the traditional
role of service provider is divided into two: the infrastruc-
ture providers who manage cloud platforms and lease re-
sources according to a usage-based pricing model, and ser-
vice providers, who rent resources from one or many in-
frastructure providers to serve the end users. The emer-
gence of cloud computing has made a tremendous impact
on the Information Technology (IT) industry over the past
few years, where large companies such as Google, Ama-
zon and Microsoft strive to provide more powerful, reliable
and cost-efficient cloud platforms, and business enterprises
seek to reshape their business models to gain benefit from
this new paradigm. Indeed, cloud computing provides sev-
eral compelling features that make it attractive to business
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

invest in the infrastructure to start gaining benefit from cloud
computing. It simply rents resources from the cloud accord-
ing to its own needs and pay for the usage.
Lowering operating cost: Resources in a cloud environ-
ment can be rapidly allocated and de-allocated on demand.
Hence, a service provider no longer needs to provision ca-
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.
Highly scalable: Infrastructure providers pool large
amount of resources from data centers and make them easily
accessible. A service provider can easily expand its service
to large scales in order to handle rapid increase in service
demands (e.g., flash-crowd effect). This model is sometimes
called surge computing [5].
mailto:[email protected]
8 J Internet Serv Appl (2010) 1: 7–18
Easy access: Services hosted in the cloud are generally
web-based. Therefore, they are easily accessible through a
variety of devices with Internet connections. These devices
not only include desktop and laptop computers, but also cell
phones and PDAs.
Reducing business risks and maintenance expenses: By
outsourcing the service infrastructure to the clouds, a service
provider shifts its business risks (such as hardware failures)
to infrastructure providers, who often have better expertise
and are better equipped for managing these risks. In addi-

tion, a service provider can cut down the hardware mainte-
nance and the staff training costs.
However, although cloud computing has shown consid-
erable opportunities to the IT industry, it also brings many
unique challenges that need to be carefully addressed. In this
paper, we present a survey of cloud computing, highlighting
its key concepts, architectural principles, state-of-the-art im-
plementations as well as research challenges. Our aim is to
provide a better understanding of the design challenges of
cloud computing and identify important research directions
in this fascinating topic.
The remainder of this paper is organized as follows. In
Sect. 2 we provide an overview of cloud computing and
compare it with other related technologies. In Sect. 3, we
describe the architecture of cloud computing and present
its design principles. The key features and characteristics of
cloud computing are detailed in Sect. 4. Section 5 surveys
the commercial products as well as the current technologies
used for cloud computing. In Sect. 6, we summarize the cur-
rent research topics in cloud computing. Finally, the paper
concludes in Sect. 7.
2 Overview of cloud computing
This section presents a general overview of cloud comput-
ing, including its definition and a comparison with related
concepts.
2.1 Definitions
The main idea behind cloud computing is not a new one.
John McCarthy in the 1960s already envisioned that com-
puting facilities will be provided to the general public like
a utility [39]. The term “cloud” has also been used in vari-

ous contexts such as describing large ATM networks in the
1990s. However, it was after Google’s CEO Eric Schmidt
used the word to describe the business model of provid-
ing services across the Internet in 2006, that the term re-
ally started to gain popularity. Since then, the term cloud
computing has been used mainly as a marketing term in a
variety of contexts to represent many different ideas. Cer-
tainly, the lack of a standard definition of cloud computing
has generated not only market hypes, but also a fair amount
of skepticism and confusion. For this reason, recently there
has been work on standardizing the definition of cloud com-
puting. As an example, the work in [49] compared over 20
different definitions from a variety of sources to confirm a
standard definition. In this paper, we adopt the definition
of cloud computing provided by The National Institute of
Standards and Technology (NIST) [36], as it covers, in our
opinion, all the essential aspects of cloud computing:
NIST definition of cloud computing Cloud computing is a
model for enabling convenient, on-demand network access
to a shared pool of configurable computing resources (e.g.,
networks, servers, storage, applications, and services) that
can be rapidly provisioned and released with minimal man-
agement effort or service provider interaction.
The main reason for the existence of different percep-
tions of cloud computing is that cloud computing, unlike
other technical terms, is not a new technology, but rather
a new operations model that brings together a set of ex-
isting technologies to run business in a different way. In-
deed, most of the technologies used by cloud computing,
such as virtualization and utility-based pricing, are not new.
Instead, cloud computing leverages these existing technolo-
gies to meet the technological and economic requirements
of today’s demand for information technology.

2.2 Related technologies
Cloud computing is often compared to the following tech-
nologies, each of which shares certain aspects with cloud
computing:
Grid Computing: Grid computing is a distributed com-
puting paradigm that coordinates networked resources to
achieve a common computational objective. The develop-
ment of Grid computing was originally driven by scien-
tific applications which are usually computation-intensive.
Cloud computing is similar to Grid computing in that it also
employs distributed resources to achieve application-level
objectives. However, cloud computing takes one step further
by leveraging virtualization technologies at multiple levels
(hardware and application platform) to realize resource shar-
ing and dynamic resource provisioning.
Utility Computing: Utility computing represents the
model of providing resources on-demand and charging cus-
tomers based on usage rather than a flat rate. Cloud comput-
ing can be perceived as a realization of utility computing. It
adopts a utility-based pricing scheme entirely for economic
reasons. With on-demand resource provisioning and utility-
based pricing, service providers can truly maximize resource
utilization and minimize their operating costs.
Virtualization: Virtualization is a technology that ab-
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-

ized server is commonly called a virtual machine (VM). Vir-
tualization forms the foundation of cloud computing, as it
provides the capability of pooling computing resources from
clusters of servers and dynamically assigning or reassigning
virtual resources to applications on-demand.
Autonomic Computing: Originally coined by IBM in
2001, autonomic computing aims at building computing sys-
tems capable of self-management, i.e. reacting to internal
and external observations without human intervention. The
goal of autonomic computing is to overcome the manage-
ment complexity of today’s computer systems. Although
cloud computing exhibits certain autonomic features such
as automatic resource provisioning, its objective is to lower
the resource cost rather than to reduce system complexity.
In summary, cloud computing leverages virtualization
technology to achieve the goal of providing computing re-
sources as a utility. It shares certain aspects with grid com-
puting and autonomic computing but differs from them in
other aspects. Therefore, it offers unique benefits and im-
poses distinctive challenges to meet its requirements.
3 Cloud computing architecture
This section describes the architectural, business and various
operation models of cloud computing.
3.1 A layered model of cloud computing
Generally speaking, the architecture of a cloud comput-
ing environment can be divided into 4 layers: the hard-
ware/datacenter layer, the infrastructure layer, the platform
layer and the application layer, as shown in Fig. 1. We de-
scribe each of them in detail:

The hardware layer: This layer is responsible for man-
aging the physical resources of the cloud, including phys-
ical servers, routers, switches, power and cooling systems.
In practice, the hardware layer is typically implemented
in data centers. A data center usually contains thousands
of servers that are organized in racks and interconnected
through switches, routers or other fabrics. Typical issues
at hardware layer include hardware configuration, fault-
tolerance, traffic management, power and cooling resource
management.
The infrastructure layer: Also known as the virtualiza-
tion layer, the infrastructure layer creates a pool of storage
and computing resources by partitioning the physical re-
sources using virtualization technologies such as Xen [55],
KVM [30] and VMware [52]. The infrastructure layer is an
essential component of cloud computing, since many key
features, such as dynamic resource assignment, are only
made available through virtualization technologies.
The platform layer: Built on top of the infrastructure
layer, the platform layer consists of operating systems and
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-
menting storage, database and business logic of typical web
applications.
The application layer: At the highest level of the hierar-
chy, the application layer consists of the actual cloud appli-
cations. Different from traditional applications, cloud appli-
cations can leverage the automatic-scaling feature to achieve
better performance, availability and lower operating cost.
Compared to traditional service hosting environments

such as dedicated server farms, the architecture of cloud
computing is more modular. Each layer is loosely coupled
with the layers above and below, allowing each layer to
evolve separately. This is similar to the design of the OSI
Fig. 1 Cloud computing
architecture
model for network protocols. The architectural modularity
allows cloud computing to support a wide range of applica-
tion requirements while reducing management and mainte-
nance overhead.
3.2 Business model
Cloud computing employs a service-driven business model.
In other words, hardware and platform-level resources are
provided as services on an on-demand basis. Conceptually,
every layer of the architecture described in the previous sec-
tion can be implemented as a service to the layer above.
Conversely, every layer can be perceived as a customer of
the layer below. However, in practice, clouds offer services
that can be grouped into three categories: software as a ser-
vice (SaaS), platform as a service (PaaS), and infrastructure
as a service (IaaS).
1. Infrastructure as a Service: IaaS refers to on-demand
provisioning of infrastructural resources, usually in terms
of VMs. The cloud owner who offers IaaS is called an
IaaS provider. Examples of IaaS providers include Ama-
zon EC2 [2], GoGrid [15] and Flexiscale [18].

2. Platform as a Service: PaaS refers to providing platform
layer resources, including operating system support and
software development frameworks. Examples of PaaS
providers include Google App Engine [20], Microsoft
Windows Azure [53] and Force.com [41].
3. Software as a Service: SaaS refers to providing on-
demand applications over the Internet. Examples of SaaS
providers include Salesforce.com [41], Rackspace [17]
and SAP Business ByDesign [44].
The business model of cloud computing is depicted by
Fig. 2. According to the layered architecture of cloud com-
puting, it is entirely possible that a PaaS provider runs its
cloud on top of an IaaS provider’s cloud. However, in the
current practice, IaaS and PaaS providers are often parts of
the same organization (e.g., Google and Salesforce). This is
why PaaS and IaaS providers are often called the infrastruc-
ture providers or cloud providers [5].
Fig. 2 Business model of cloud computing
3.3 Types of clouds
There are many issues to consider when moving an enter-
prise application to the cloud environment. For example,
some service providers are mostly interested in lowering op-
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:
Public clouds: A cloud in which service providers of-
fer their resources as services to the general public. Pub-
lic clouds offer several key benefits to service providers, in-
cluding no initial capital investment on infrastructure and
shifting of risks to infrastructure providers. However, pub-

lic clouds lack fine-grained control over data, network and
security settings, which hampers their effectiveness in many
business scenarios.
Private clouds: Also known as internal clouds, private
clouds are designed for exclusive use by a single organiza-
tion. A private cloud may be built and managed by the orga-
nization or by external providers. A private cloud offers the
highest degree of control over performance, reliability and
security. However, they are often criticized for being simi-
lar to traditional proprietary server farms and do not provide
benefits such as no up-front capital costs.
Hybrid clouds: A hybrid cloud is a combination of public
and private cloud models that tries to address the limitations
of each approach. In a hybrid cloud, part of the service in-
frastructure runs in private clouds while the remaining part
runs in public clouds. Hybrid clouds offer more flexibility
than both public and private clouds. Specifically, they pro-
vide tighter control and security over application data com-
pared to public clouds, while still facilitating on-demand
service expansion and contraction. On the down side, de-
signing a hybrid cloud requires carefully determining the
best split between public and private cloud components.
Virtual Private Cloud: An alternative solution to address-
ing the limitations of both public and private clouds is called
Virtual Private Cloud (VPC). A VPC is essentially a plat-
form running on top of public clouds. The main difference is
that a VPC leverages virtual private network (VPN) technol-
ogy that allows service providers to design their own topol-
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,
certain types of clouds will be more popular than others.
http://Force.com
http://Salesforce.com
In particular, it was predicted that hybrid clouds will be the
dominant type for most organizations [14]. However, vir-
tual private clouds have started to gain more popularity since
their inception in 2009.
4 Cloud computing characteristics
Cloud computing provides several salient features that are
different from traditional service computing, which we sum-
marize below:
Multi-tenancy: In a cloud environment, services owned
by multiple providers are co-located in a single data center.
The performance and management issues of these services
are shared among service providers and the infrastructure
provider. The layered architecture of cloud computing pro-
vides a natural division of responsibilities: the owner of each
layer only needs to focus on the specific objectives associ-
ated with this layer. However, multi-tenancy also introduces
difficulties in understanding and managing the interactions
among various stakeholders.

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-
source assignment capability provides much flexibility to in-
frastructure providers for managing their own resource us-
age and operating costs. For instance, an IaaS provider can
leverage VM migration technology to attain a high degree
of server consolidation, hence maximizing resource utiliza-
tion while minimizing cost such as power consumption and
cooling.
Geo-distribution and ubiquitous network access: Clouds
are generally accessible through the Internet and use the
Internet as a service delivery network. Hence any device
with Internet connectivity, be it a mobile phone, a PDA or
a laptop, is able to access cloud services. Additionally, to
achieve high network performance and localization, many
of today’s clouds consist of data centers located at many
locations around the globe. A service provider can easily
leverage geo-diversity to achieve maximum service utility.
Service oriented: As mentioned previously, cloud com-
puting adopts a service-driven operating model. Hence it
places a strong emphasis on service management. In a cloud,
each IaaS, PaaS and SaaS provider offers its service accord-
ing to the Service Level Agreement (SLA) negotiated with
its customers. SLA assurance is therefore a critical objective
of every provider.
Dynamic resource provisioning: One of the key features
of cloud computing is that computing resources can be ob-
tained and released on the fly. Compared to the traditional
model that provisions resources according to peak demand,
dynamic resource provisioning allows service providers to
acquire resources based on the current demand, which can

considerably lower the operating cost.
Self-organizing: Since resources can be allocated or de-
allocated on-demand, service providers are empowered to
manage their resource consumption according to their own
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
the flash crowd effect.
Utility-based pricing: Cloud computing employs a pay-
per-use pricing model. The exact pricing scheme may vary
from service to service. For example, a SaaS provider may
rent a virtual machine from an IaaS provider on a per-hour
basis. On the other hand, a SaaS provider that provides
on-demand customer relationship management (CRM) may
charge its customers based on the number of clients it serves
(e.g., Salesforce). Utility-based pricing lowers service oper-
ating cost as it charges customers on a per-use basis. How-
ever, it also introduces complexities in controlling the oper-
ating cost. In this perspective, companies like VKernel [51]
provide software to help cloud customers understand, ana-
lyze and cut down the unnecessary cost on resource con-
sumption.
5 State-of-the-art
In this section, we present the state-of-the-art implementa-
tions of cloud computing. We first describe the key technolo-
gies currently used for cloud computing. Then, we survey
the popular cloud computing products.
5.1 Cloud computing technologies
This section provides a review of technologies used in cloud
computing environments.

5.1.1 Architectural design of data centers
A data center, which is home to the computation power and
storage, is central to cloud computing and contains thou-
sands of devices like servers, switches and routers. Proper
planning of this network architecture is critical, as it will
heavily influence applications performance and throughput
in such a distributed computing environment. Further, scala-
bility and resiliency features need to be carefully considered.
Currently, a layered approach is the basic foundation of
the network architecture design, which has been tested in
some of the largest deployed data centers. The basic layers
of a data center consist of the core, aggregation, and access
layers, as shown in Fig. 3. The access layer is where the
servers in racks physically connect to the network. There
are typically 20 to 40 servers per rack, each connected to an
access switch with a 1 Gbps link. Access switches usually
connect to two aggregation switches for redundancy with
Fig. 3 Basic layered design of data center network infrastructure
10 Gbps links. The aggregation layer usually provides im-
portant functions, such as domain service, location service,
server load balancing, and more. The core layer provides
connectivity to multiple aggregation switches and provides
a resilient routed fabric with no single point of failure. The
core routers manage traffic into and out of the data center.
A popular practice is to leverage commodity Ethernet
switches and routers to build the network infrastructure. In

different business solutions, the layered network infrastruc-
ture can be elaborated to meet specific business challenges.
Basically, the design of a data center network architecture
should meet the following objectives [1, 21–23, 35]:
Uniform high capacity: The maximum rate of a server-
to-server traffic flow should be limited only by the available
capacity on the network-interface cards of the sending and
receiving servers, and assigning servers to a service should
be independent of the network topology. It should be possi-
ble for an arbitrary host in the data center to communicate
with any other host in the network at the full bandwidth of
its local network interface.
Free VM migration: Virtualization allows the entire VM
state to be transmitted across the network to migrate a VM
from one physical machine to another. A cloud comput-
ing hosting service may migrate VMs for statistical multi-
plexing or dynamically changing communication patterns
to achieve high bandwidth for tightly coupled hosts or to
achieve variable heat distribution and power availability in
the data center. The communication topology should be de-
signed so as to support rapid virtual machine migration.
Resiliency: Failures will be common at scale. The net-
work infrastructure must be fault-tolerant against various
types of server failures, link outages, or server-rack failures.
Existing unicast and multicast communications should not
be affected to the extent allowed by the underlying physical
connectivity.
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-
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-
ots at the same time. Rather than the three-layered approach
discussed above, Guo et al. [22, 23] proposed server-centric,
recursively defined network structures of MDC.
5.1.2 Distributed file system over clouds
Google File System (GFS) [19] is a proprietary distributed
file system developed by Google and specially designed to
provide efficient, reliable access to data using large clusters
of commodity servers. Files are divided into chunks of 64
megabytes, and are usually appended to or read and only
extremely rarely overwritten or shrunk. Compared with tra-
ditional file systems, GFS is designed and optimized to run
on data centers to provide extremely high data throughputs,
low latency and survive individual server failures.
Inspired by GFS, the open source Hadoop Distributed
File System (HDFS) [24] stores large files across multi-
ple machines. It achieves reliability by replicating the data
across multiple servers. Similarly to GFS, data is stored on
multiple geo-diverse nodes. The file system is built from a

cluster of data nodes, each of which serves blocks of data
over the network using a block protocol specific to HDFS.
Data is also provided over HTTP, allowing access to all con-
tent from a web browser or other types of clients. Data nodes
can talk to each other to rebalance data distribution, to move
copies around, and to keep the replication of data high.
5.1.3 Distributed application framework over clouds
HTTP-based applications usually conform to some web ap-
plication framework such as Java EE. In modern data center
environments, clusters of servers are also used for computa-
tion and data-intensive jobs such as financial trend analysis,
or film animation.
MapReduce [16] is a software framework introduced by
Google to support distributed computing on large data sets
on clusters of computers. MapReduce consists of one Mas-
ter, to which client applications submit MapReduce jobs.
The Master pushes work out to available task nodes in the
data center, striving to keep the tasks as close to the data
as possible. The Master knows which node contains the
data, and which other hosts are nearby. If the task cannot
be hosted on the node where the data is stored, priority is
given to nodes in the same rack. In this way, network traffic
on the main backbone is reduced, which also helps to im-
prove throughput, as the backbone is usually the bottleneck.
If a task fails or times out, it is rescheduled. If the Master
fails, all ongoing tasks are lost. The Master records what it
is up to in the filesystem. When it starts up, it looks for any
such data, so that it can restart work from where it left off.

The open source Hadoop MapReduce project [25] is in-
spired by Google’s work. Currently, many organizations are
using Hadoop MapReduce to run large data-intensive com-
putations.
5.2 Commercial products
In this section, we provide a survey of some of the dominant
cloud computing products.
5.2.1 Amazon EC2
Amazon Web Services (AWS) [3] is a set of cloud services,
providing cloud-based computation, storage and other func-
tionality that enable organizations and individuals to deploy
applications and services on an on-demand basis and at com-
modity prices. Amazon Web Services’ offerings are acces-
sible over HTTP, using REST and SOAP protocols.
Amazon Elastic Compute Cloud (Amazon EC2) enables
cloud users to launch and manage server instances in data
centers using APIs or available tools and utilities. EC2 in-
stances are virtual machines running on top of the Xen virtu-
alization engine [55]. After creating and starting an instance,
users can upload software and make changes to it. When
changes are finished, they can be bundled as a new machine
image. An identical copy can then be launched at any time.
Users have nearly full control of the entire software stack
on the EC2 instances that look like hardware to them. On
the other hand, this feature makes it inherently difficult for
Amazon to offer automatic scaling of resources.
EC2 provides the ability to place instances in multiple lo-
cations. EC2 locations are composed of Regions and Avail-
ability Zones. Regions consist of one or more Availability

Zones, are geographically dispersed. Availability Zones are
distinct locations that are engineered to be insulated from
failures in other Availability Zones and provide inexpensive,
low latency network connectivity to other Availability Zones
in the same Region.
EC2 machine images are stored in and retrieved from
Amazon Simple Storage Service (Amazon S3). S3 stores
data as “objects” that are grouped in “buckets.” Each object
contains from 1 byte to 5 gigabytes of data. Object names
are essentially URI [6] pathnames. Buckets must be explic-
itly created before they can be used. A bucket can be stored
in one of several Regions. Users can choose a Region to opti-
mize latency, minimize costs, or address regulatory require-
ments.
Amazon Virtual Private Cloud (VPC) is a secure and
seamless bridge between a company’s existing IT infrastruc-
ture and the AWS cloud. Amazon VPC enables enterprises
to connect their existing infrastructure to a set of isolated
AWS compute resources via a Virtual Private Network
(VPN) connection, and to extend their existing management
capabilities such as security services, firewalls, and intrusion
detection systems to include their AWS resources.
For cloud users, Amazon CloudWatch is a useful man-
agement tool which collects raw data from partnered AWS
services such as Amazon EC2 and then processes the in-
formation into readable, near real-time metrics. The metrics
about EC2 include, for example, CPU utilization, network
in/out bytes, disk read/write operations, etc.
5.2.2 Microsoft Windows Azure platform
Microsoft’s Windows Azure platform [53] consists of three

components and each of them provides a specific set of ser-
vices to cloud users. Windows Azure provides a Windows-
based environment for running applications and storing data
on servers in data centers; SQL Azure provides data services
in the cloud based on SQL Server; and .NET Services offer
distributed infrastructure services to cloud-based and local
applications. Windows Azure platform can be used both by
applications running in the cloud and by applications run-
ning on local systems.
Windows Azure also supports applications built on the
.NET Framework and other ordinary languages supported in
Windows systems, like C#, Visual Basic, C++, and others.
Windows Azure supports general-purpose programs, rather
than a single class of computing. Developers can create web
applications using technologies such as ASP.NET and Win-
dows Communication Foundation (WCF), applications that
run as independent background processes, or applications
that combine the two. Windows Azure allows storing data
in blobs, tables, and queues, all accessed in a RESTful style
via HTTP or HTTPS.
SQL Azure components are SQL Azure Database and
“Huron” Data Sync. SQL Azure Database is built on Mi-
crosoft SQL Server, providing a database management sys-
tem (DBMS) in the cloud. The data can be accessed using
ADO.NET and other Windows data access interfaces. Users
can also use on-premises software to work with this cloud-
based information. “Huron” Data Sync synchronizes rela-
tional data across various on-premises DBMSs.
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
applications
Traditional web applications
with supported framework
Computation OS Level on a Xen Virtual
Machine
Microsoft Common Language
Runtime (CLR) VM; Predefined
roles of app. instances
Predefined web application
frameworks
Storage Elastic Block Store; Amazon
Simple Storage Service (S3);
Amazon SimpleDB
Azure storage service and SQL
Data Services
BigTable and MegaStore
Auto Scaling Automatically changing the
number of instances based on
parameters that users specify

Automatic scaling based on
application roles and a
configuration file specified by
users
Automatic Scaling which is
transparent to users
The .NET Services facilitate the creation of distributed
applications. The Access Control component provides a
cloud-based implementation of single identity verification
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
the cloud. Each exposed endpoint is assigned a URI, which
clients can use to locate and access a service.
All of the physical resources, VMs and applications in
the data center are monitored by software called the fabric
controller. With each application, the users upload a config-
uration file that provides an XML-based description of what
the application needs. Based on this file, the fabric controller
decides where new applications should run, choosing phys-
ical servers to optimize hardware utilization.
5.2.3 Google App Engine
Google App Engine [20] is a platform for traditional web
applications in Google-managed data centers. Currently, the
supported programming languages are Python and Java.
Web frameworks that run on the Google App Engine include
Django, CherryPy, Pylons, and web2py, as well as a custom
Google-written web application framework similar to JSP
or ASP.NET. Google handles deploying code to a cluster,
monitoring, failover, and launching application instances as

necessary. Current APIs support features such as storing and
retrieving data from a BigTable [10] non-relational database,
making HTTP requests and caching. Developers have read-
only access to the filesystem on App Engine.
Table 1 summarizes the three examples of popular cloud
offerings in terms of the classes of utility computing, tar-
get types of application, and more importantly their models
of computation, storage and auto-scaling. Apparently, these
cloud offerings are based on different levels of abstraction
and management of the resources. Users can choose one
type or combinations of several types of cloud offerings to
satisfy specific business requirements.
6 Research challenges
Although cloud computing has been widely adopted by the
industry, the research on cloud computing is still at an early
stage. Many existing issues have not been fully addressed,
while new challenges keep emerging from industry applica-
tions. In this section, we summarize some of the challenging
research issues in cloud computing.
6.1 Automated service provisioning
One of the key features of cloud computing is the capabil-
ity of acquiring and releasing resources on-demand. The ob-
jective of a service provider in this case is to allocate and
de-allocate resources from the cloud to satisfy its service
level objectives (SLOs), while minimizing its operational
cost. However, it is not obvious how a service provider can
achieve this objective. In particular, it is not easy to de-
termine how to map SLOs such as QoS requirements to
low-level resource requirement such as CPU and memory
requirements. Furthermore, to achieve high agility and re-

spond to rapid demand fluctuations such as in flash crowd
effect, the resource provisioning decisions must be made on-
line.
Automated service provisioning is not a new problem.
Dynamic resource provisioning for Internet applications has
been studied extensively in the past [47, 57]. These ap-
proaches typically involve: (1) Constructing an application
performance model that predicts the number of application
instances required to handle demand at each particular level,
in order to satisfy QoS requirements; (2) Periodically pre-
dicting future demand and determining resource require-
ments using the performance model; and (3) Automatically
allocating resources using the predicted resource require-
ments. Application performance model can be constructed
using various techniques, including Queuing theory [47],
Control theory [28] and Statistical Machine Learning [7].
Additionally, there is a distinction between proactive
and reactive resource control. The proactive approach uses
predicted demand to periodically allocate resources before
they are needed. The reactive approach reacts to immedi-
ate demand fluctuations before periodic demand prediction
is available. Both approaches are important and necessary
for effective resource control in dynamic operating environ-
ments.
6.2 Virtual machine migration
Virtualization can provide significant benefits in cloud com-
puting by enabling virtual machine migration to balance

load across the data center. In addition, virtual machine mi-
gration enables robust and highly responsive provisioning in
data centers.
Virtual machine migration has evolved from process
migration techniques [37]. More recently, Xen [55] and
VMWare [52] have implemented “live” migration of VMs
that involves extremely short downtimes ranging from tens
of milliseconds to a second. Clark et al. [13] pointed out that
migrating an entire OS and all of its applications as one unit
allows to avoid many of the difficulties faced by process-
level migration approaches, and analyzed the benefits of live
migration of VMs.
The major benefits of VM migration is to avoid hotspots;
however, this is not straightforward. Currently, detecting
workload hotspots and initiating a migration lacks the agility
to respond to sudden workload changes. Moreover, the in-
memory state should be transferred consistently and effi-
ciently, with integrated consideration of resources for appli-
cations and physical servers.
6.3 Server consolidation
Server consolidation is an effective approach to maximize
resource utilization while minimizing energy consumption
in a cloud computing environment. Live VM migration tech-
nology is often used to consolidate VMs residing on multi-
ple under-utilized servers onto a single server, so that the
remaining servers can be set to an energy-saving state. The
problem of optimally consolidating servers in a data center
is often formulated as a variant of the vector bin-packing
problem [11], which is an NP-hard optimization problem.
Various heuristics have been proposed for this problem
[33, 46]. Additionally, dependencies among VMs, such as

communication requirements, have also been considered re-
cently [34].
However, server consolidation activities should not hurt
application performance. It is known that the resource usage
(also known as the footprint [45]) of individual VMs may
vary over time [54]. For server resources that are shared
among VMs, such as bandwidth, memory cache and disk
I/O, maximally consolidating a server may result in re-
source congestion when a VM changes its footprint on the
server [38]. Hence, it is sometimes important to observe
the fluctuations of VM footprints and use this information
for effective server consolidation. Finally, the system must
quickly react to resource congestions when they occur [54].
6.4 Energy management
Improving energy efficiency is another major issue in cloud
computing. It has been estimated that the cost of powering
and cooling accounts for 53% of the total operational expen-
diture of data centers [26]. In 2006, data centers in the US
consumed more than 1.5% of the total energy generated in
that year, and the percentage is projected to grow 18% an-
nually [33]. Hence infrastructure providers are under enor-
mous pressure to reduce energy consumption. The goal is
not only to cut down energy cost in data centers, but also to
meet government regulations and environmental standards.
Designing energy-efficient data centers has recently re-
ceived considerable attention. This problem can be ap-
proached from several directions. For example, energy-
efficient hardware architecture that enables slowing down
CPU speeds and turning off partial hardware components
[8] has become commonplace. Energy-aware job schedul-
ing [50] and server consolidation [46] are two other ways to
reduce power consumption by turning off unused machines.

Recent research has also begun to study energy-efficient net-
work protocols and infrastructures [27]. A key challenge in
all the above methods is to achieve a good trade-off between
energy savings and application performance. In this respect,
few researchers have recently started to investigate coordi-
nated solutions for performance and power management in
a dynamic cloud environment [32].
6.5 Traffic management and analysis
Analysis of data traffic is important for today’s data cen-
ters. For example, many web applications rely on analysis
of traffic data to optimize customer experiences. Network
operators also need to know how traffic flows through the
network in order to make many of the management and plan-
ning decisions.
However, there are several challenges for existing traf-
fic measurement and analysis methods in Internet Service
Providers (ISPs) networks and enterprise to extend to data
centers. Firstly, the density of links is much higher than
that in ISPs or enterprise networks, which makes the worst-
case scenario for existing methods. Secondly, most existing
methods can compute traffic matrices between a few hun-
dreds end hosts, but even a modular data center can have
several thousand servers. Finally, existing methods usually
assume some flow patterns that are reasonable in Internet
and enterprises networks, but the applications deployed on
data centers, such as MapReduce jobs, significantly change
the traffic pattern. Further, there is tighter coupling in appli-
cation’s use of network, computing, and storage resources,

than what is seen in other settings.
Currently, there is not much work on measurement and
analysis of data center traffic. Greenberg et al. [21] report
data center traffic characteristics on flow sizes and concur-
rent flows, and use these to guide network infrastructure de-
sign. Benson et al. [16] perform a complementary study of
traffic at the edges of a data center by examining SNMP
traces from routers.
6.6 Data security
Data security is another important research topic in cloud
computing. Since service providers typically do not have ac-
cess to the physical security system of data centers, they
must rely on the infrastructure provider to achieve full
data security. Even for a virtual private cloud, the service
provider can only specify the security setting remotely, with-
out knowing whether it is fully implemented. The infrastruc-
ture provider, in this context, must achieve the following
objectives: (1) confidentiality, for secure data access and
transfer, and (2) auditability, for attesting whether secu-
rity setting of applications has been tampered or not. Con-
fidentiality is usually achieved using cryptographic proto-
cols, whereas auditability can be achieved using remote at-
testation techniques. Remote attestation typically requires a
trusted platform module (TPM) to generate non-forgeable
system summary (i.e. system state encrypted using TPM’s
private key) as the proof of system security. However, in a
virtualized environment like the clouds, VMs can dynami-
cally migrate from one location to another, hence directly
using remote attestation is not sufficient. In this case, it is
critical to build trust mechanisms at every architectural layer
of the cloud. Firstly, the hardware layer must be trusted
using hardware TPM. Secondly, the virtualization platform
must be trusted using secure virtual machine monitors [43].

VM migration should only be allowed if both source and
destination servers are trusted. Recent work has been de-
voted to designing efficient protocols for trust establishment
and management [31, 43].
6.7 Software frameworks
Cloud computing provides a compelling platform for host-
ing large-scale data-intensive applications. Typically, these
applications leverage MapReduce frameworks such as
Hadoop for scalable and fault-tolerant data processing. Re-
cent work has shown that the performance and resource con-
sumption of a MapReduce job is highly dependent on the
type of the application [29, 42, 56]. For instance, Hadoop
tasks such as sort is I/O intensive, whereas grep requires
significant CPU resources. Furthermore, the VM allocated
to each Hadoop node may have heterogeneous character-
istics. For example, the bandwidth available to a VM is
dependent on other VMs collocated on the same server.
Hence, it is possible to optimize the performance and cost
of a MapReduce application by carefully selecting its con-
figuration parameter values [29] and designing more effi-
cient scheduling algorithms [42, 56]. By mitigating the bot-
tleneck resources, execution time of applications can be
significantly improved. The key challenges include perfor-
mance modeling of Hadoop jobs (either online or offline),
and adaptive scheduling in dynamic conditions.
Another related approach argues for making MapReduce
frameworks energy-aware [50]. The essential idea of this ap-
proach is to turn Hadoop node into sleep mode when it has
finished its job while waiting for new assignments. To do so,
both Hadoop and HDFS must be made energy-aware. Fur-
thermore, there is often a trade-off between performance and
energy-awareness. Depending on the objective, finding a de-

sirable trade-off point is still an unexplored research topic.
6.8 Storage technologies and data management
Software frameworks such as MapReduce and its various
implementations such as Hadoop and Dryad are designed
for distributed processing of data-intensive tasks. As men-
tioned previously, these frameworks typically operate on
Internet-scale file systems such as GFS and HDFS. These
file systems are different from traditional distributed file sys-
tems in their storage structure, access pattern and application
programming interface. In particular, they do not implement
the standard POSIX interface, and therefore introduce com-
patibility issues with legacy file systems and applications.
Several research efforts have studied this problem [4, 40].
For instance, the work in [4] proposed a method for sup-
porting the MapReduce framework using cluster file sys-
tems such as IBM’s GPFS. Patil et al. [40] proposed new
API primitives for scalable and concurrent data access.
6.9 Novel cloud architectures
Currently, most of the commercial clouds are implemented
in large data centers and operated in a centralized fashion.
Although this design achieves economy-of-scale and high
manageability, it also comes with its limitations such high
energy expense and high initial investment for construct-
ing data centers. Recent work [12, 48] suggests that small-
size data centers can be more advantageous than big data
centers in many cases: a small data center does not con-
sume so much power, hence it does not require a power-

ful and yet expensive cooling system; small data centers are
cheaper to build and better geographically distributed than
large data centers. Geo-diversity is often desirable for re-
sponse time-critical services such as content delivery and
interactive gaming. For example, Valancius et al. [48] stud-
ied the feasibility of hosting video-streaming services using
application gateways (a.k.a. nano-data centers).
Another related research trend is on using voluntary re-
sources (i.e. resources donated by end-users) for hosting
cloud applications [9]. Clouds built using voluntary re-
sources, or a mixture of voluntary and dedicated resources
are much cheaper to operate and more suitable for non-profit
applications such as scientific computing. However, this ar-
chitecture also imposes challenges such managing heteroge-
neous resources and frequent churn events. Also, devising
incentive schemes for such architectures is an open research
problem.
7 Conclusion
Cloud computing has recently emerged as a compelling par-
adigm for managing and delivering services over the Inter-
net. The rise of cloud computing is rapidly changing the
landscape of information technology, and ultimately turning
the long-held promise of utility computing into a reality.
However, despite the significant benefits offered by cloud
computing, the current technologies are not matured enough
to realize its full potential. Many key challenges in this
domain, including automatic resource provisioning, power
management and security management, are only starting
to receive attention from the research community. There-
fore, we believe there is still tremendous opportunity for re-
searchers to make groundbreaking contributions in this field,
and bring significant impact to their development in the in-

dustry.
In this paper, we have surveyed the state-of-the-art of
cloud computing, covering its essential concepts, architec-
tural designs, prominent characteristics, key technologies as
well as research directions. As the development of cloud
computing technology is still at an early stage, we hope our
work will provide a better understanding of the design chal-
lenges of cloud computing, and pave the way for further re-
search in this area.
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/NLD (Gebruik deze instellingen om Adobe PDF-documenten
te maken die zijn geoptimaliseerd voor weergave op een
beeldscherm, e-mail en internet. De gemaakte PDF-documenten
kunnen worden geopend met Acrobat en Adobe Reader 5.0 en
hoger.)
/NOR
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/ENU (Use these settings to create Adobe PDF documents
best suited for on-screen display, e-mail, and the Internet.
Created PDF documents can be opened with Acrobat and Adobe
Reader 5.0 and later.)
/DEU
<FEFF004a006f0062006f007000740069006f006e007300200066
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>>
/Namespace [
(Adobe)
(Common)
(1.0)
]
/OtherNamespaces [
<<
/AsReaderSpreads false

/CropImagesToFrames true
/ErrorControl /WarnAndContinue
/FlattenerIgnoreSpreadOverrides false
/IncludeGuidesGrids false
/IncludeNonPrinting false
/IncludeSlug false
/Namespace [
(Adobe)
(InDesign)
(4.0)
]
/OmitPlacedBitmaps false
/OmitPlacedEPS false
/OmitPlacedPDF false
/SimulateOverprint /Legacy
>>
<<
/AddBleedMarks false
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/AddPageInfo false
/AddRegMarks false
/ConvertColors /ConvertToRGB
/DestinationProfileName (sRGB IEC61966-2.1)
/DestinationProfileSelector /UseName
/Downsample16BitImages true
/FlattenerPreset <<
/PresetSelector /MediumResolution
>>
/FormElements false
/GenerateStructure false
/IncludeBookmarks false
/IncludeHyperlinks false
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/IncludeLayers false
/IncludeProfiles true

/MultimediaHandling /UseObjectSettings
/Namespace [
(Adobe)
(CreativeSuite)
(2.0)
]
/PDFXOutputIntentProfileSelector /NA
/PreserveEditing false
/UntaggedCMYKHandling /UseDocumentProfile
/UntaggedRGBHandling /UseDocumentProfile
/UseDocumentBleed false
>>
]
>> setdistillerparams
<<
/HWResolution [2400 2400]
/PageSize [595.276 841.890]
>> setpagedevice

J Internet Serv Appl (2010) 1 7–18DOI 10.1007s13174-010-00.docx

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J Internet Serv Appl (2010) 1 7–18DOI 10.1007s13174-010-00.docx