Cc unit 1 ppt

DR.P.VISU
PROFESSOR
DEPT OF CSE
VELAMMAL ENGINEERING COLLEGE

OBJECTIVES:
*To understand the concept of cloud computing.
*To appreciate the evolution of cloud from the
existing technologies.
*To have knowledge on the various issues in cloud
computing.
*To be familiar with the lead players in cloud.
*To appreciate the emergence of cloud as the next
generation computing paradigm.

 COURSE OUTCOMES:
On Completion of the course, the students should be able to:
CO1: Articulate the main concepts, key technologies,
strengths and limitations of cloud computing.
CO2: Learn the key and enabling technologies that help in
the development of cloud.
CO3: Develop the ability to understand and use the
architecture of compute and storage cloud, service and
delivery models.
CO4: Explain the core issues of cloud computing such as
resource management and security.
CO5: Be able to install and use current cloud technologies.
CO6: Evaluate and choose the appropriate technologies,
algorithms and approaches for implementation and use of
cloud.

Syllabus
UNIT I - INTRODUCTION 9
Introduction to Cloud Computing – Definition of Cloud – Evolution of Cloud
Computing – Underlying Principles of Parallel
and Distributed Computing – Cloud Characteristics – Elasticity in Cloud – On-
demand Provisioning.
UNIT II - CLOUD ENABLING TECHNOLOGIES 10
Service Oriented Architecture – REST and Systems of Systems – Web
Services – Publish- Subscribe Model – Basics of Virtualization – Types of
Virtualization – Implementation Levels of Virtualization – Virtualization
Structures – Tools and Mechanisms – Virtualization of CPU – Memory – I/O
Devices –Virtualization Support and Disaster Recovery.

Syllabus
UNIT III CLOUD ARCHITECTURE, SERVICES AND STORAGE 8
Layered Cloud Architecture Design – NIST Cloud Computing Reference
Architecture – Public, Private and Hybrid Clouds - laaS – PaaS – SaaS –
Architectural Design Challenges – Cloud Storage – Storage-as-a-Service –
Advantages of Cloud Storage – Cloud Storage Providers – S3.
UNIT IV - RESOURCE MANAGEMENT AND SECURITY IN CLOUD
10
Inter Cloud Resource Management – Resource Provisioning and Resource
Provisioning Methods – Global Exchange of Cloud Resources – Security
Overview – Cloud Security Challenges – Software-as-a-Service Security –
Security Governance – Virtual Machine Security – IAM – Security Standards.

Syllabus
UNIT V CLOUD TECHNOLOGIES AND ADVANCEMENTS 8
Hadoop – MapReduce – Virtual Box -- Google App Engine – Programming
Environment for Google App Engine –– Open Stack – Federation in the Cloud
– Four Levels of Federation – Federated Services and Applications – Future
of Federation.
TEXT BOOKS:
1. Kai Hwang, Geoffrey C. Fox, Jack G. Dongarra, "Distributed and Cloud
Computing, From Parallel Processing to the Internet of Things", Morgan
Kaufmann Publishers, 2012.
2. Rittinghouse, John W., and James F. Ransome, ―Cloud Computing:
Implementation, Management and Security‖, CRC Press, 2017

What is Cloud Computing
Basic Concepts
Deployment Models
service model
Saas – software as a service
paas – Platform as a service
iaas – infrastructure as a service
virtualization
opportunities & challenges
benefits
disadvantages
features

What is Cloud Computing?
Cloud Computing is a general term used to describe a new
class of network based computing that takes place over
the Internet,
– basically a step on from Utility Computing
– a collection/group of integrated and networked hardware,
software and Internet infrastructure (called a platform).
– Using the Internet for communication and transport provides
hardware, software and networking services to clients
These platforms hide the complexity and details of the
underlying infrastructure from users and applications by
providing very simple graphical interface or API
(Applications Programming Interface).

In addition, the platform provides on demand services, that
are always on, anywhere, anytime and any place.
• Pay for use and as needed,
– elastic scale up and down in capacity and
functionalities
• The hardware and software services are available to
– general public, enterprises, corporations and
businesses markets

Cloud computing is an umbrella term used to refer to
Internet based development and services
A number of characteristics define cloud data,
applications services and infrastructure:
– Remotely hosted: Services or data are hosted on remote
infrastructure.
– Ubiquitous: Services or data are available from anywhere.
– Commoditized: The result is a utility computing model
similar to traditional that of traditional utilities, like gas
and electricity - you pay for what you would want!

Many companies are delivering services from the
cloud. Some notable examples include the following:
– Google — Has a private cloud that it uses for delivering
Google Docs and many other services to its users,
including email access, document applications, text
translations, maps, web analytics, and much more.
– Microsoft — Has Microsoft@ Office 3650 online service
that allows for content and business intelligence tools to
be moved into the cloud, and Microsoft currently makes its
office applications available in a cloud.
– Salesforce.com — Runs its application set for its customers
in a cloud, and its Force.com and Vmforce.com products
provide developers with platforms to build customized
cloud services.

5 Real-World Examples of Cloud
Computing
• Ex: Dropbox, Gmail, Facebook.
• Ex: Maropost for Marketing, Hubspot, Adobe
Marketing Cloud.
• Ex: SlideRocket, Ratatype, Amazon
Web Services.
• Ex: ClearDATA, Dell's Secure Healthcare Cloud,
IBM Cloud.
• Uses: IT consolidation, shared services,
citizen services.

Basic Concepts
Basic Concepts There are certain services and
models working behind the scene making the
cloud computing feasible and accessible to
end users.
Following are the working models for cloud
computing:
1. Deployment Models
2. Service Models

Deployment Models
Deployment models define the type of access to
the cloud, i.e., how the cloud is located?
Cloud can have any of the four types of
access:
Public, Private, Hybrid and Community.

Deployment Models
PUBLIC CLOUD : The Public Cloud allows systems and services
to be easily accessible to the general public. Public cloud may
be less secure because of its openness, e.g., e-mail.
PRIVATE CLOUD : The Private Cloud allows systems and
services to be accessible within an organization. It offers
increased security because of its private nature.
COMMUNITY CLOUD : The Community Cloud allows systems
and services to be accessible by group of organizations.
HYBRID CLOUD : The Hybrid Cloud is mixture of public and
private cloud. However, the critical activities are performed
using private cloud while the non- critical activities are
performed using public cloud.

Service Models
Service Models are the reference models on
which the Cloud Computing is based. These
can be categorized into three basic service
models as listed below:
1. Infrastructure as a Service (laaS)
2. Platform as a Service (PaaS)
3. Software as a Service (SaaS)

Service Models - IaaS
Infrastructure as a Service (laaS) laaS is the
delivery of technology infrastructure as an on
demand scalable service.
laaS provides access to fundamental resources
such as physical machines, virtual machines,
virtual storage, etc.
Usually billed based on usage
Usually multi tenant virtualized environment
Can be coupled with Managed Services for OS and application
support

Service Models - PaaS
Platform as a Service (PaaS) the runtime environment
for applications, PaaS provides development &
deployment tools, etc. PaaS provides all of the facilities
required to support the complete life cycle of building
and delivering web applications and services entirely
from the Internet.
Typically applications must be developed with a
particular platform in mind.
•Multi tenant environments
•Highly scalable multi tier architecture

Service Models - SaaS
Software as a Service (SaaS) SaaS model allows to use
software applications as a service to end users. SaaS is a
software delivery methodology that provides licensed multi-
tenant access to software and its functions remotely as a
Web-based service.
– Usually billed based on usage
– Usually multi tenant environment
– Highly scalable architecture

Virtualization
Virtualization Virtual workspaces:
• An abstraction of an execution environment that can be made
dynamically available to authorized clients by using well-defined
protocols,
• Resource quota (e.g. CPU, memory share),
• Software configuration (e.g. O/S, provided services).
Implement on Virtual Machines (VMS):
• Abstraction of a physical host machine,
• Hvpervisor intercepts and emulates instructions from VMS, and
allows management of
• VMS, VMWare, Xen, etc.
Provide infrastructure API:
• Plug-ins to hardware/support structures

Virtualization
Virtualization in General
Advantages of virtual machines:
• Run operating systems where the physical hardware is
unavailable,
• Easier to create new machines, backup machines, etc.,
• Software testing using "clean" installs of operating systems
and software,
• Emulate more machines than are physically available,
• Timeshare lightly loaded systems on one host,
• Debug problems (suspend and resume the problem machine),
• Easy migration of virtual machines (shutdown needed or not).
• Run legacy systems!

What is the Purpose and Benefits ?
Cloud computing enables companies and applications, which are system
infrastructure dependent, to be infrastructure-less.
By using the Cloud infrastructure on "pay as used and on demand", all of us
can save in capital and operational investment!
Clients can:
Put their data on the platform instead of on their own desktop PCs and/or
on their own servers.
They can put their applications on the cloud and use the servers within
the cloud to do processing and data manipulations etc.

Cloud - Sourcing
Why is it becoming a Big Deal:
– Using high-scale/low-cost providers,
– Any time/place access via web browser,
– Rapid scalability; incremental cost and load sharing,
– Can forget need to focus on local IT.
Concerns:
– Performance, Reliability, and SLAs,
– Control of data, and service parameters,
– Application features and choices,
– Interaction between Cloud providers,
– No standard API - mix of SOAP and REST!
– Privacy, security, compliance, trust

The use of the cloud provides a number of opportunities:
 It enables services to be used without any understanding of their
infrastructure.
 Cloud computing works using economies of scale:
 It potentially lowers the outlay expense for start up companies, as they
would no longer need to buy their own software or servers.
 Cost would be by on-demand pricing.
 Vendors and Service providers claim costs by establishing an ongoing
revenue stream.
 Data and services are stored remotely but accessible from
"anywhere".

In parallel there has been backlash against cloud computing:
 Use of cloud computing means dependence on others and that could
possibly limit flexibility and innovation:
 The others are likely become the bigger Internet companies like
Google and IBM, who may monopolies the market.
 Some argue that this use of supercomputers is a return to the time
of mainframe computing that the PC was a reaction against.
 Security could prove to be a big issue:
 It is still unclear how safe out-sourced data is and when using these
services ownership of data is not always clear

There are also issues relating to policy and access:
 If your data is stored abroad whose policy do you adhere to?
 What happens if the remote server goes down?
 How will you then access files?
 There have been cases of users being locked out of accounts and
losing access to data.

 Cost Savings - Companies can reduce their capital expenditures and use operational
expenditures for increasing their computing capabilities. This is a lower barrier to entry
and also requires fewer in-house IT resources to provide system support.
 Scalability/Flexibility — Companies can start with a small deployment and grow to a
large deployment fairly rapidly, and then scale back if necessary. Also, the flexibility of
cloud computing allows companies to use extra resources at peak times, enabling them
to satisfy consumer demands.
 Reliability — Services using multiple redundant sites can support business continuity
and disaster recovery.
 Maintenance — Cloud service providers do the system maintenance, and access is
through APIs that do not require application installations onto PCs, thus further
reducing maintenance requirements.
 Mobile Accessible — Mobile workers have increased productivity due to systems
accessible in n-infrastructure-available-from-anywhere

 Requires a constant Internet connection:
 Cloud computing is impossible if you cannot connect to the
Internet.
 Since you use the Internet to connect to both your applications and
documents, if you do not have an Internet connection you cannot
access anything, even your own documents.
 A dead Internet connection means no work and in areas where
Internet connections are few or inherently unreliable, this could be
a deal-breaker

Stored data might not be secure:
 With cloud computing, all your data is stored on the cloud.
 The questions is How secure is the cloud?
 Can unauthorized users gain access to your confidential data?
Stored data can be lost:
 Theoretically, data stored in the cloud is safe, replicated across
multiple machines.
 But on the off chance that your data goes missing, you have no
physical or local backup.
 Put simply, relying on the cloud puts you at risk if the cloud
lets you down.

 Many of the activities loosely grouped together under
cloud computing have already been happening and
centralized computing activity is not a new phenomena
 Grid Computing was the last research-led centralized
approach
 However there are concerns that the mainstream
adoption of cloud computing could cause many
problems for users
 Many new open source systems appearing that you can
install and run on your local cluster
 should be able to run a variety of applications on
these systems

Definition Of Cloud
The term cloud has been used historically as a metaphor
for the Internet. This usage was originally derived from its
common description in network diagrams as an outline of
a cloud, used to represent the transport of data across
carrier backbones (which owned the cloud) to an endpoint
location on the other side of the cloud.
A simple definition of cloud computing involves delivering different types of
services over the Internet. From software and analytics to secure and safe data
storage and networking resources, everything can be delivered via the cloud.
Cloud Computing is the use of commodity hardware and software
computing resources to delivered an infinite elastic online public utility.

The Emergence of Cloud Computing
Utility computing can be defined as the provision of
computational and storage resources as a metered
service, similar to those provided by a traditional
public utility company.
This, of course, is not a new idea. This form of
computing is growing in popularity, however, as
companies have begun to extend the model to a cloud
computing paradigm providing virtual servers that IT
departments and users can access on demand.

The Global Nature of the Cloud
The cloud sees no borders and thus has made the
world a much smaller place. The Internet is global in
scope but respects only established communication
paths. People from everywhere now have access to
other people from anywhere else.
Globalization of computing assets may be the biggest
contribution the cloud has made to date. For this
reason, the cloud is the subject of many complex
geopolitical issues.

Grid Computing
or
Cloud Computing?
Grid computing is often confused with cloud computing. Grid
computing is a form of distributed computing that implements
a virtual supercomputer made up of a cluster of networked or
Internetworked computers acting in unison to perform very
large tasks.
Many cloud computing deployments today are powered by
grid computing implementations and are billed like utilities,
but cloud computing can and should be seen as an evolved
next step away from the grid utility model.

Is the Cloud Model Reliable?
The majority of today’s cloud computing
infrastructure consists of time-tested and highly
reliable services built on servers with varying levels
of virtualized technologies, which are delivered via
large data centers operating under service-level
agreements that require 99.99% or better uptime.
Commercial offerings have evolved to meet the
quality-of-service requirements of customers and
typically offer such service-level agreements to their
customers

What About Legal Issues When
Using Cloud Models?
1. Notify individuals about the purposes for which information is
collected and used.
2. Give individuals the choice of whether their information can be
disclosed to a third party.
3. Ensure that if it transfers personal information to a third party,
that third party also provides the same level of privacy
protection.
4. Allow individuals access to their personal information.
5. Take reasonable security precautions to protect collected data
from loss, misuse, or disclosure.
6. Take reasonable steps to ensure the integrity of the data
collected.
7. Have in place an adequate enforcement mechanism.

What Are the Key Characteristics of
Cloud Computing?
• Centralization of infrastructure and lower costs
• Increased peak-load capacity
• Efficiency improvements for systems that are often
underutilized
• Dynamic allocation of CPU, storage, and network bandwidth
• Consistent performance that is monitored by the provider of
the service

The Evolution of Cloud
Computing
It is important to understand the evolution of computing in
order to get an appreciation of how we got into the cloud
environment. Looking at the evolution of the computing
hardware itself, from the first generation to the current (fourth)
generation of computers, shows how we got from there to
here.
The hardware, however, was only part of the evolutionary
process. As hardware evolved, so did software. As networking
evolved, so did the rules for how computers communicate. The
development of such rules, or protocols, also helped drive the
evolution of Internet software.

Hardware Evolution –
First-Generation Computers
The Harvard Mark I computer.
(Image from www.columbia.edu/acis/history/mark1.html, retrieved 9 Jan 2009.)
It was a general-purpose electromechanical programmable computer.
Mark I was designed and developed at Harvard University in 1943.

Hardware Evolution
The British-developed Colossus computer.
(Image from www.computerhistory.org, retrieved 9 Jan 2009.)

Second-Generation Computers
The ENIAC computer. (Image from www.mrsec.wisc.edu/.../computer/
eniac.html, retrieved 9 Jan 2009.)
Another general-purpose computer of this era was ENIAC (Electronic
Numerical Integrator and Computer, shown in Figure 1.3), which was
built in 1946.

Third-Generation Computers
The Intel 4004 processor. (Image from www.thg.ru/cpu/20051118/
index.html, retrieved 9 Jan 2009.)
Even though first integrated circuit was produced in September
1958, microchips were not used in computers until 1963.

Fourth-Generation Computers
The fourth-generation computers that were being
developed at this time utilized a microprocessor that
put the computer’s processing capabilities on a single
integrated circuit chip. By combining random access
memory (RAM), developed by Intel, fourth-
generation computers were faster than ever before
and had much smaller footprints. The PC era had
begun in earnest by the mid-1980s.

Internet Software Evolution
Vannevar Bush’s MEMEX. (Image from www.icesi.edu.co/
blogs_estudiantes/luisaulestia, retrieved 9 Jan 2009.)
First individuals Vannervar Bush introduced the concept of the MEMEX in
the 1930s as a microfilm-based “device in which an individual stores all his
books, records, and communications.
The conceptual foundation for creation of the Internet was significantly
developed by three individuals.

The second individual to have a profound effect in shaping the Internet was
Norbert Wiener. Wiener was an early pioneer in the study of stochastic and
noise processes. His work in stochastic and noise processes was relevant to
electronic engineering, communication, and control systems. He also
founded the field of cybernetics.
Marshall McLuhan put forth the idea of a global village that was
interconnected by an electronic nervous system as part of our popular culture.
In 1957, the Soviet Union launched the first satellite, Sputnik I, prompting
U.S. President Dwight Eisenhower to create the Advanced Research
Projects Agency (ARPA) agency to regain the technological lead in the
arms race.

The SAGE system. (Image from USAF Archives, retrieved from http://
history.sandiego.edu/GEN/recording/images5/PDRM0380.jpg.)
ARPA (renamed DARPA, the Defense Advanced Research Projects Agency, in 1972)
appointed J. C. R. Licklider to head the new Information Processing Techniques Office
(IPTO). Licklider was given a mandate to further the research of the SAGE system. The
SAGE system (see Figure) was a continental air-defense network commissioned by the U.S.
military and designed to help protect the United States against a space based nuclear attack.
SAGE stood for Semi-Automatic Ground Environment.

Licklider worked for several years at ARPA, where he set the stage for the
creation of the ARPANET. He also worked at Bolt Beranek and Newman
(BBN), the company that supplied the first computers connected on the
ARPANET.
So, as it turned out, the first networking protocol that was used on the
ARPANET was the Network Control Program (NCP). The NCP provided
the middle layers of a protocol stack running on an ARPANET-connected
host computer.
After he had left ARPA, Licklider succeeded in convincing his replacement
to hire a man named Lawrence Roberts, believing that Roberts was just the
person to implement Licklider’s vision of the future network computing
environment.
An application layer, built on top of the NCP, provided services such as
email and file transfer. These applications used the NCP to handle
connections to other host computers.

An Interface Message Processor. (Image from luni.net/wp-content/
uploads/2007/02/bbn-imp.jpg, retrieved 9 Jan 2009.)
A minicomputer was created specifically to realize the design of the Interface
Message Processor(IMP). This approach provided a system-independent
interface to the ARPANET that could be used by any computer system.

IMPArchitecture
Overview of the IMP architecture.

Internet Software Evolution - Establishing a
Common Protocol for the Internet
Since the lower-level protocol layers were
provided by the IMP host interface, the NCP
essentially provided a transport layer
consisting of the ARPANET Host-to-Host
Protocol (AHHP) and the Initial Connection
Protocol (ICP). The AHHP specified how to
transmit a unidirectional, flow-controlled data
stream between two hosts.

Internet Software Evolution –
Evolution of Ipv6
The amazing growth of the Internet throughout the
1990s caused a vast reduction in the number of free
IP addresses available under IPv4. IPv4 was never
designed to scale to global levels. To increase
available address space, it had to process data packets
that were larger (i.e., that contained more bits of
data). This resulted in a longer IP address and that
caused problems for existing hardware and software.

Building a Common Interface to the Internet
While Marc Andreessen and the NCSA team were
working on their browsers, Robert Cailliau at CERN
independently proposed a project to develop a
hypertext system. He joined forces with Berners-Lee
to get the web initiative into high gear. Cailliau
rewrote his original proposal and lobbied CERN
management for funding for programmers. He and
Berners-Lee worked on papers and presentations in
collaboration, and Cailliau helped run the very first
WWW conference.

The first web browser, created by Tim Berners-Lee. (Image from
www.tranquileye.com/cyber/index.html, retrieved 9 Jan 2009.)

The original NCSA Mosaic browser. (Image from http://www.nsf.gov/od/lpa/news/03/images/mosaic.6beta.jpg.)

Server Virtualization
Virtualization is a method of running multiple
independent virtual operating systems on a single
physical computer. This approach maximizes the
return on investment for the computer.
The term was coined in the 1960s in reference to a
virtual machine (sometimes called a pseudo-
machine). The creation and management of virtual
machines has often been called platform
virtualization.

Underlying Principles of Parallel
and
Distributed Computing
The terms parallel computing and distributed
computing are often used interchangeably, even
though they means lightly different things. The term
parallel implies a tightly coupled system, whereas
distributed refers to a wider class of system, including
those that are tightly coupled

and
Eras of computing,1940s - 2030s

and
More precisely, the term parallel computing refers to a
model in which the computation is divided among several
processors sharing the same memory. The architecture of
a parallel computing system is often characterized by the
homogeneity of components: each processor is of the
same type and it has the same capability as the others.
The shared memory has a single address space, which is
accessible to all the processors. Parallel programs are then
broken down into several units of execution that can be
allocated to different processors and can communicate
with each other by means of the shared memory.

Elements of parallel computing
The first steps in this direction led to the development
of parallel computing, which encompasses
techniques, architectures, and systems for performing
multiple activities in parallel. As we already
discussed, the term parallel computing has blurred its
edges with the term distributed computing

What is parallel processing?
Processing of multiple tasks simultaneously on
multiple processors is called parallel processing. The
parallel program consists of multiple active
processes(tasks) simultaneously solving a given
problem.
A given task is divided into multiple subtasks using a
divide-and-conquer technique, and each sub task is
processed on a different central processing unit
(CPU). Programming on a multiprocessor system
using the divide-and-conquer technique is called
parallel programming.

What is parallel processing?
The development of parallel processing is being influenced by many
factors. The prominent among them include the following:
– Computational requirements are ever increasing in the areas of both scientific
and business computing.
– Sequential architectures are reaching physical limitations as they are
constrained by the speed of light and thermodynamics laws.
– Hardware improvements in pipelining, superscalar, and the like are nonscalable
and require sophisticated compiler technology.
– Vector processing works well for certain kinds of problems. It is suitable
mostly for scientific problems (involving lots of matrix operations) and
graphical processing.
– The technology of parallel processing is mature and can be exploited
commercially; there is already significant R&D work on development tools and
environments.
– Significant development in networking technology is paving the way for
heterogeneous computing.

Hardware architectures for parallel
processing
The core elements of parallel processing are CPUs. Based on
the number of instruction and data streams that can be
processed simultaneously, computing systems are classified
into the following four categories
• Single-instruction, single-data (SISD) systems
•Single-instruction, multiple-data (SIMD) systems
•Multiple-instruction, single-data (MISD) systems
•Multiple-instruction, multiple-data (MIMD) systems

Single-instruction, single-data
(SISD) systems
An SISD computing system is a uniprocessor machine capable
of executing a single instruction, which operates on a single
data stream. In SISD, machine instructions are processed
sequentially; hence computers adopting this model are
popularly called sequential computers. Most conventional
computers are built using the SISD model.
Single-instruction, single-data (SISD) architecture.

Single-instruction, multiple-data
(SIMD) systems
An SIMD computing system is a multiprocessor machine capable of
executing the same instruction on all the CPUs but operating on different
data streams
Single-instruction, multiple-data (SIMD) architecture.

Multiple-instruction, single-data
(MISD) systems
An MISD computing system is a multiprocessor machine capable of
executing different instructions on different Processing Elements (PEs) but
all of them operating on the same data set
Multiple-instruction, Single-data (MISD) architecture.

Multiple-instruction, multiple-data
(MIMD) systems
An MIMD computing system is a multiprocessor machine capable of
executing multiple instructions on multiple data sets Each PE in the MIMD
model has separate instruction and data streams; hence machines built
using this model are well suited to any kind of application. Unlike SIMD
and MISD machines, PEs in MIMD machines work asynchronously.
Multiple-instruction, Multiple-data (MIMD) architecture.

Shared memory MIMD machines
In the shared memory MIMD model, all the PEs are connected to a single
global memory and they all have access to it Systems based on this model
are also called tightly coupled multiprocessor systems.
Shared (left) and distributed (right) memory MIMD architecture.

Approaches to parallel programming
A sequential program is one that runs on a single processor and has a single
line of control. To make many processors collectively work on a single
program, the program must be divided into smaller independent chunks so
that each processor can work on separate chunks of the problem.
A wide variety of parallel programming approaches are available. The most
prominent among them are the following:
• Data parallelism
• Process parallelism
• Farmer-and-worker model
These three models are all suitable for task-level parallelism. In the case of
data parallelism, the divide-and-conquer technique is used to split data into
multiple sets, and each data set is processed on different PEs using the
same instruction.

Levels of parallelism
Levels of parallelism are decided based on the lumps of code (grain size)
that can be a potential candidate for parallelism. Their approaches have a
common goal: to boost processor efficiency by hiding latency.

Elements of Distributed computing
we extend these concepts and explore how multiple
activities can be performed by leveraging systems
composed of multiple heterogeneous machines and
systems.

General concepts and definitions
of Distributed Computing
A distributed system is a collection of independent
computers that appears to its users as a single coherent
system.
A distributed system is one in which components located
at networked computers communicate and coordinate
their actions only by passing messages.
As specified in this definition, the components of a
distributed system communicate with some sort of
message passing. This is a term that encompasses several
communication models.

Components of a distributed system
A distributed system is the result of the interaction of
several components that traverse the entire computing
stack from hardware to software. It emerges from the
collaboration of several elements that—by working
together—give users the illusion of a single coherent
system.

A layered view of a distributed system.

A cloud computing distributed system.

Architectural styles for distributed
computing
Architectural styles are mainly used to determine the
vocabulary of components and connectors that are
used as instances of the style together with a set of
constraints on how they can be combined.
We organize the architectural styles into two major
classes:
• Software architectural styles
• System architectural styles

Cloud Characteristics
Five essential characteristics of Cloud Computing
1. On demand self-service
2. Broad network access
3. Resource pooling
4. Rapid Elasticity
5. Measured service

On demand self-service
Computer services such as Email, Application
Network, or Server service can be provided without
requiring interaction with each service provider.
Self-service means that the consumer performs all
the actions needed to acquire the service himself,
instead of going through an IT department. For
example – The consumer’s request is then
automatically processed by the cloud infrastructure,
without human intervention on the provider’s side.

Broad Network Access
Cloud capabilities are available over the network
and accessed through standard mechanism that
promote use by heterogeneous client such as mobile
phone, laptop

Resource pooling
– The providers computing resources are pooled together to serve
multiple customers, with different physical and virtual resources
dynamically assigned and reassigned according to the customers
demand.
– There is a sense of location independence in that the customer
generally has no control or knowledge over the exact location of the
provided resources but may be able to specify location at a higher
level of abstraction (e.g. country, state, or datacenter).
– Example of resources include storage, processing, memory, and
network bandwidth.

Rapid elasticity
– Capabilities can be elastically provisioned and released, in
some cases automatically, to scale rapidly outward and inward
commensurate with demand.
– To the consumer, the capabilities available for provisioning
often appear to be unlimited and can be appropriated in any
quantity at any time.

Measured service
– Cloud systems automatically control and optimize resource use by
leveraging a metering capability at some level of abstraction
appropriate to the type of service (e.g. storage, processing,
bandwidth, and active use account).
– Resource usage can be monitored, controlled, and reported,
providing transparency for both the provider and consumer of the
utilized service.

Multi-tenancy
In a private cloud, the customers are also called tenants, can have
different business divisions inside the same company. In a public
cloud, the customers are often entirely different organizations.
Most public cloud providers use the multi-tenancy model. Multi-
tenancy allows customers to run one server instance, which is less
expensive and makes it easier to deploy updates to a large number of
customers.

Elasticity in Cloud
Elastic computing is nothing but a concept in cloud computing in
which computing resources can be scaled up and down easily by
the cloud service provider. Cloud service provider gives you
provision to flexible computing power when and wherever
required. The elasticity of these resources depends upon the
following factors such as processing power, storage, bandwidth,
etc.

Types of Elastic Cloud Computing
While scalability can rely on elasticity, it can also be
achieved with over provisioning.
There are two types of scalability:

The first option is Scale Vertically or Scale-Up –
this type of scalability can work with any application
to a limited degree. In an elastic environment,
scaling up would be accomplished by moving the
application to a bigger virtual machine or by resizing
the VM.

The second option is Scale Horizontally or Scale-out,
by provisioning more instances of the application tiers
on additional virtual machines and then dividing the
load between them.

Horizontal scaling is similar to elasticity; it allows
the re-division of resources between applications by
provisioning, or by claiming back virtual machines.
Horizontal scaling uses the infrastructure elasticity,
but the application needs to be able to scale by
adding more nodes and by distributing the load.

Is there any difference between
Scalability and Elasticity?
Then answer is yes. Scalability refers to the ability of
system to accommodate larger loads just by adding
resources either making hardware stronger (scale up)
or adding additional nodes (scale out).

Benefits/Pros of Elastic Cloud
Computing
Elastic Cloud Computing has numerous advantages.
Some of them are as follow:-
1. Cost Efficiency: - Cloud is available at much cheaper rates
than traditional approaches and can significantly lower the
overall IT expenses. By using cloud solution companies can
save licensing fees as well as eliminate overhead charges such
as the cost of data storage, software updates, management etc.

Computing
2. Convenience and continuous availability: - Cloud makes
easier access of shared documents and files with view and
modify choice. Public clouds also offer services that are
available wherever the end user might be located. Moreover it
guaranteed continuous availability of resources and In case of
system failure; alternative instances are automatically
spawned on other machines.

Computing
3. Backup and Recovery: - The process of backing up and
recovering data is easy as information is residing on cloud
simplified and not on a physical device. The various cloud
providers offer reliable and flexible backup/recovery
solutions.

Computing
4. Cloud is environmentally friendly:-The cloud is more
efficient than the typical IT infrastructure and it takes fewer
resources to compute, thus saving energy.

Computing
5. Scalability and Performance: - Scalability is a built-in
feature for cloud deployments. Cloud instances are deployed
automatically only when needed and as a result enhance
performance with excellent speed of computations.

Computing
6. Increased Storage Capacity:-The cloud can accommodate
and store much more data compared to a personal computer and
in a way offers almost unlimited storage capacity.

Elasticity in Cloud Computing
Cloud Computing or Cloud is defined as using various
services such as software development platforms, servers,
storage, over the Internet.
Elastic computing is nothing but a concept in cloud
computing in which computing resources can be scaled up
and down easily by the cloud service provider. Cloud service
provider gives you provision to flexible computing power
when and wherever required. The elasticity of these resources
depends upon the following factors such as processing power,
storage, bandwidth, etc.

Schematic Example of an (unrealistically) ideal elastic System with immediate and
fully-compensating elasticity:

However, in reality, resources are actually measured and
provisioned in larger discrete units (i.e. one processor core,
processor time slices, one page of main memory, etc.), so a
continuous idealistic scaling/elasticity cannot be achieved.
On an elastic cloud platform, the performance metric (here:
response time) will rise as workload intensity increases until
a certain threshold is reached at which the cloud platform
will provide additional resources.

Schematic Example of an Elastic System

Definition
•Changes in resource demands or explicit scaling requests trigger run time
adaptations of the amount of resources that an execution platform provides to
applications.
•The magnitude of these changes depends on the current and previous state of
the execution platform, and also on the current and previous behavior of the
applications running on that platform.
•Consequently, elasticity is a multi-valued metric that depends on several run
time factors. This is reflected by the following definitions, which are illustrated
by Fig 13.
•Elasticity of execution platforms consists of the temporal and quantitative
properties of runtime resource provisioning and un-provisioning, performed by
the execution platform; execution platform elasticity depends on the state of the
platform and on the state of the platform-hosted applications.

Reconfiguration point is a time point at which a platform adaptation (resource
provisioning or un-provisioning) is processed by the system.
Elasticity Metrics
There are several characteristics of resource elasticity, which are parameterised by
the platform state/history, application state/history and workload state/history:
Effect of reconfiguration is quantified by the amount of added/removed resources
and thus expresses the granularity of possible reconfigurations/adaptations.
Temporal distribution of reconfiguration points describes the density of
reconfiguration points over a possible interval of a resource’s usage amounts or over
a time interval in relation to the density of changes in workload intensity.
Provisioning time or reaction time is the time interval between the instant when a
reconfiguration has been triggered/requested until the adaptation has been completed.
An example for provisioning time would be the time between the request for an
additional thread and the instant of actually holding it.

Elasticity in cloud

Direct and Indirect Measuring of Elasticity Metrics
Directly on the execution platform
In general, the effects of scalability are visible to the user/client via
changing response times or throughput values at a certain scaling
level of the system. On the other hand, the elasticity, namely the
resource resizing actions, may not be directly visible to the client
due to their shortness or due to client’s limited access to an
execution platform’s state and configuration.

“Independent workload element
For elasticity measurements on any elastic system, it is necessary to
fill the system with a variable intensity of workloads. The workload
itself consists of small independent workload elements that are
supposed to run concurrently and designed to stress mainly one
specific resource type (like Fibonacci calculation for CPU or an
array sort for memory).

“Independent workload element” means in this case that there is no
interdependency between the workload elements that would require
communication or synchronisation and therefore induce overheads. It
is necessary to stress mainly the “resource under test”, to avoid
bottlenecks elsewhere in the system.

As the concepts of resource elasticity are validated in the following
example using Java thread pools as virtual resources provided by a
Java Virtual Machine. Java thread pools are designed to grow and
shrink dynamically in size, while still trying to reuse idle resources.

In general , differentiate between the following orders of
values and can be applied to the Java thread pool example.
.

Two different Approaches for Measuring Provisioning Times

If we measure elasticity of a virtual resource that
shares physical resources with other tasks, no precise
view on a correlation between cause and effect will be
given anymore when trying to interpret the
characteristics of waiting and processing times. In this
case observing response times does not allow direct
and exact extraction of elasticity metrics.

The provisioning times of a elastic system, which were
defined as the delay between a trigger event and the
visibility of a resource reallocation, cannot be
measured directly without having access to system
internal log files and setting them in relation with
measured resource amounts.

If a new workload task is added and cannot be served
directly by the system, we assume that a trigger event
for resizing is created within the execution platform.
Not every trigger event results in a resource
reallocation. In addition, the measurement log files
on execution platform side must be enabled to keep
track of any changes in resource amounts

On-demand Provisioning.
It simply requires these two things to be true:
•The service must be always available (or some
reasonable approximation of always)
•The service received must be modifiable by the
client organization without contacting the hosting
provider. It is the second that is typically the most
difficult to meet.

While the public providers like Amazon, Google, and
Microsoft have this facet, smaller niche providers
typically do not. This is more likely when the provider is
also supplying services or managed hosting for the
application itself, especially during an Infrastructure as a
Service (IaaS)- type scenario. In enterprise application
hosting scenarios, there are also potential contractual
issues to consider when decreasing (or even possibly
increasing) capacity without interacting with the vendor.

It is important to determine these issues before
making changes to your own environment. If your
service/uptime SLAs require a certain level of
hardware to support, remember to ensure that you do
not compromise them by unduly changing the
capacity available to your applications.

Cloud Computing for
On-Demand Resource Provisioning

Objectives
• Show the benefits of the separation of resource
provisioning from job execution management for HPC, cluster
and grid computing
• Introduce OpenNEbula as the Engine for on-demand resource
provisioning
• Present Cloud Computing as a paradigm for the on-demand
provision of virtualized resources as a service
• Describe Grid as the interoperability technology for the
federation of clouds
• Introduce the RESERVOIR project as the infrastructure
technology to support the setup and deployment of services and
resources on-demand across administrative domains

Contents
1. Local On-demand Resource Provisioning
1.1. The Engine for the Virtual Infrastructure
1.2. Virtualization of Cluster and HPC Systems
1.3. Benefits
1.4. Related Work
2. Remote On-demand Resource Provisioning
2.1. Access to Cloud Systems
2.2. Federation of Cloud Systems
3. Conclusions

1. Local on-Demand Resource
Provisioning
1. Local On-demand Resource Provisioning
1.1. The Engine for the Virtual Infrastructure
• OpenNEbula creates a distributed virtualization layer
• Extend the benefits of VM Monitors from one to multiple resources
• Decouple the VM (service) from the physical location
• Transform a distributed physical infrastructure into a flexible and
elastic virtual infrastructure, which adapts to the changing demands
of the VM (service) workloads
Any service, not only
cluster working nodes

Provisioning
1.2. Virtualization of Cluster and HPC Systems
Separation of Resource Provisioning from Job Management
• New virtualization layer between the service and the infrastructure layers
• Seamless integration with the existing middleware stacks.
• Completely transparent to the computing service and so end users

Provisioning
1.3. Benefits

Provisioning

Provisioning
1.3. Benefits
Benefits for Existing Grid Infrastructures (EGEE, TeraGrid…)
• The virtualization of the local infrastructure supports a virtualized
alternative to contribute resources to a Grid infrastructure
• Simpler deployment and operation of new middleware distributions
• Lower operational costs
• Easy provision of resources to more than one infrastructure or VO
• Easy support for VO-specific worker nodes
• Performance partitioning between local and grid clusters

Provisioning
1.4. Related Works
Integration of Job Execution Managers with Virtualization
• VMs to Provide pre-Created Software Environments for Jobs
• Extensions of job execution managers to create per-job basis VMs so
as to provide a pre-defined environment for job execution
• Those approaches still manage jobs
• The VMs are bounded to a given PM and only exist during job
execution
• Condor, SGE, MOAB, Globus GridWay…
• Job Execution Managers for the Management of VMs
• Job execution managers enhanced to allow submission of VMs
• Those approaches manage VMs as jobs
• Condor, “pilot” backend in Globus VWS…

Provisioning
1.4. Related Works
Differences between Job and VM Management
• Differences between VMs and Jobs as basic Management Entities
• VM structure: Images with fixed and variable parts for migration…
• VM life-cycle: Fixed and transient states for contextualization, live
migration…
• VM duration: Long time periods (“forever”)
• VM groups (services): Deploy ordering, affinity, rollback
management…
• VM elasticity: Changing of capacity requirements and number of VMs
• Different Metrics in the Allocation of Physical Resources
• Capacity provisioning: Probability of SLA violation for a given cost of
provisioning including support for server consolidation, partitioning…
• HPC scheduling: Turnaround time, wait time, throughput…

Provisioning
1.4. Related Works
Other Tools for VM Management
• VMware DRS, Platform Orchestrator, IBM Director, Novell ZENworks,
Enomalism, Xenoserver…
• Advantages:
• Open-source (Apache license v2.0)
• Open and flexible architecture to integrate new virtualization
technologies
• Support for the definition of any scheduling policy (consolidation,
workload balance, affinity, SLA…)
• LRM-like CLI and API for the integration of third-party tools

2. Remote on-Demand Resource
Provisioning
• Provision of virtualized resources as a service
VM Management Interfaces
• Submission
• Control
• Monitoring
INFRASTRUCTURE CLOUD COMPUTING SOLUTIONS
• Commercial Cloud: Amazon EC2
• Scientific Cloud: Nimbus (University of Chicago)
• Open-source Technologies
• Globus VWS (Globus interfaces)
• Eucalyptus (Interfaces compatible with Amazon EC2)
• OpenNEbula (Engine for the Virtual Infrastructure)

Provisioning
On-demand Access to Cloud Resources
• Supplement local resources with cloud resources to satisfy peak or irregular
demands

Provisioning
2.2. Federation of Cloud Systems
Grid and Cloud are Complementary
• Grid interfaces and protocols enable the interoperability between the clouds
or infrastructure providers
• Grid as technology for federation of administrative domains (not as
infrastructure for job computing)
• Grid infrastructures for computing are one of the service use cases that
could run on top of the cloud

3. Conclusions
• Show the benefits of the separation of resource provisioning from
job execution management for HPC, cluster and grid computing
• Introduce OpenNEbula as the Engine for the local Virtual
Infrastructure
• Present Cloud Computing as a paradigm for the on-demand
provision of virtualized resources as a service
• Describe Grid as the interoperability technology for the federation of
clouds

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