Cloud Computing_Module-1.pdf

Cloud Computing
Sweta Kumari Barnwal
THE VISION OF CLOUD COMPUTING
In Simplest terms, cloud computing means storing and accessing the data and programs on
remote servers that are hosted on internet instead of computer’s hard drive or local server.
Cloud computing is also referred as Internet based computing. These are following Vision of
Cloud Computing:
1. Cloud computing provides the facility to provision virtual hardware, runtime environment
and services to a person having money.
2. These all things can be used as long as they are needed by the user.
3. The whole collection of computing system is transformed into collection of utilities, which
can be provisioned and composed together to deploy systems in hours rather than days,
with no maintenance cost.
4. The long term vision of a cloud computing is that IT services are traded as utilities in an
open market without technological and legal barriers.
5. In the future, we can imagine that it will be possible to find the solution that matches with
our requirements by simply entering out request in a global digital market that trades with
cloud computing services.
6. The existence of such market will enable the automation of discovery process and its
integration into its existing software systems.
7. Due to the existence of a global platform for trading cloud services will also help service
providers to potentially increase their revenue.
8. A cloud provider can also become a consumer of a competition service in order to fulfill its
promises to customers.
9. In the near future we can imagine a solution that suits our needs by simply applying our
application to the global digital market for cloud computing services.
10. The presence of this market will enable the acquisition process to automatically integrate
with its integration into its existing software applications. The availability of a global cloud
trading platform will also help service providers to increase their revenue.
11. A cloud provider can also be a buyer of a competitive service to fulfill its promises to
customers.
THE CLOUD COMPUTING REFERENCE MODEL
Cloud computing enables convenient, on-demand network access to a shared pool of computing
resources. It allows rapid development and release of your services with minimal management
effort or any other service provider interaction.
Cloud computing continues to evolve from a market disruptor to the expected approach for IT. It
now impacts every aspect of modern businesses. It also serves as the foundation for other
disruptive trends, including the Internet of Things, artificial intelligence (AI) and the digital
business.
This figure appearing below illustrates the benefits of cloud computing:

Cloud Computing
Figure: Benefits of Cloud Computing
As you can see, cloud computing enables businesses to focus on their core strengths and delegate
non-strategic IT functions to an external partner. This allows companies to improve scalability,
agility, increase innovation and redu
CLOUD REFERENCE MODELS
With the increasing popularity of cloud computing, the definitions of various cloud computing
architectures have broadened. The cloud landscape has many vendors and multiple definitions of
offerings, making it very difficult to evaluate their services. With such diversity in their
implementation, understanding how the cloud works and interacts with other technologies can be
a little confusing.
To achieve the potential of cloud computing, there is a need to have a sta
model for the software architects, software engineers, security experts and businesses, since it
provides a fundamental reference point for the development of cloud computing. The Cloud
Reference Model brings order to this cloud lands
various cloud providers and their technologies within the available cloud service models in the
market.
efits of Cloud Computing
strategic IT functions to an external partner. This allows companies to improve scalability,
agility, increase innovation and reduce costs.
CLOUD REFERENCE MODELS
difficult to evaluate their services. With such diversity in their
To achieve the potential of cloud computing, there is a need to have a standard
for the software architects, software engineers, security experts and businesses, since it
Reference Model brings order to this cloud landscape. This figure appearing here also illustrates
strategic IT functions to an external partner. This allows companies to improve scalability,
difficult to evaluate their services. With such diversity in their
ndard cloud reference
for the software architects, software engineers, security experts and businesses, since it
cape. This figure appearing here also illustrates

Cloud Computing
Figure: Cloud Service Models
The Model Overview
Standards bodies define the cloud reference models,
models. The National Institute of Standards and Technology (NIST), a U.S. government body,
defines the standard protocol for cloud computing providers. Vendors then develop their cloud
platform while following the defined standard guidelines and starting their operational
engagement with enterprise business systems.
NIST took an early leadership role in standardizing the definitions around cloud computing. This
happened as cloud technology was making inroads into
Leading cloud service providers map their services to the NIST reference model. IBM,
Microsoft, Oracle, and Amazon are leading companies in the market, which provide cloud
service that comply with the NIST reference model. The
 Describes the domain and key concepts
 Provides a shared vision, understanding, and taxonomy
 Reduces complexity to enhance the development of new products
 Provides a guide for interoperability and standards
 Provides a model for functions a
 Defines best practices
An Example and Definition
The NIST cloud computing reference model identifies the major actors, their activities, and
functions in cloud computing. This figure appearing here, depicts the NIST hig
architecture, which defines the requirements, uses, characteristics, and standards of cloud
computing:
1. Infrastructure as a service (IaaS)
: Cloud Service Models
Standards bodies define the cloud reference models, and then vendors map their services to these
he defined standard guidelines and starting their operational
engagement with enterprise business systems.
happened as cloud technology was making inroads into the U.S. federal government.
service that comply with the NIST reference model. The reference model:
Describes the domain and key concepts
Provides a shared vision, understanding, and taxonomy
Reduces complexity to enhance the development of new products
Provides a guide for interoperability and standards
Provides a model for functions and services of cloud implementations
functions in cloud computing. This figure appearing here, depicts the NIST hig
1. Infrastructure as a service (IaaS)
and then vendors map their services to these
he defined standard guidelines and starting their operational
the U.S. federal government.
nd services of cloud implementations
functions in cloud computing. This figure appearing here, depicts the NIST high-level model or

Cloud Computing
Infrastructure as a service (IaaS) is a cloud computing offering in which a vendor provides users
access to computing resources such as servers, storage and networking. To read more about IaaS
click here.
2. Platform as a service (PaaS)
Platform as a service (PaaS) is a cloud computing offering that provides users with a cloud
environment in which they can develop manage and deliver applications. To read more about
PaaS click here.
3. Software as a service (SaaS)
Software as a service (SaaS) is a cloud computing offering that provides users with access to a
vendor’s cloud-based software. Users do not install applications on their local devices. Instead,
the applications reside on a remote cloud network accessed through the web or an API. Through
the application, users can store and analyze data and collaborate on projects.
CHARACTERISTICS OF CLOUD COMPUTING
There are basically 5 essential characteristics of Cloud Computing.
1. On-demand self-services: The Cloud computing services does not require any human
administrators, user they are able to provision, monitor and manage computing resources as
needed.
2. Broad network access: The Computing services are generally provided over standard
networks and heterogeneous devices.
3. Rapid elasticity: The Computing services should have IT resources that are able to scale
out and in quickly and on as needed basis. Whenever the user require services it is provided
to him and it is scale out as soon as its requirement gets over.
4. Resource pooling: The IT resource (e.g., networks, servers, storage, applications, and
services) present are shared across multiple applications and occupant in an uncommitted
manner. Multiple clients are provided service from a same physical resource.
5. Measured service: The resource utilization is tracked for each application and occupant; it
will provide both the user and the resource provider with an account of what has been used.
This is done for various reasons like monitoring billing and effective use of resource.
ADVANTAGES OF CLOUD COMPUTING
As we all know that Cloud computing is trending technology. Almost every company switched
their services on the cloud to raise the company growth. Here, we are going to discuss some
important advantages of Cloud Computing-

Cloud Computing
1) Back-up and restore data: Once the data is stored in the cloud, it is easier to get back
restore that data using the cloud.
2) Improved collaboration: Cloud applications improve collaboration by allowing groups of
people to quickly and easily share informati
3) Excellent accessibility: Cloud allows us to
anywhere, anytime in the whole world, using an internet connection. An internet cloud
infrastructure increases organization productivity and efficiency by ensuring that our data is
always accessible.
4) Low maintenance cost: Cloud computing reduces both hardware and software maintenance
costs for organizations.
5) Mobility: Cloud computing allows us to easily access all cloud data via mobile.
6) Services in the pay-per-
Interfaces (APIs) to the users for access services on the cloud and pays the charges as per the
usage of service.
7) Unlimited storage capacity
important data such as document
8) Data security: Data security is one of the biggest advantages of cloud computing. Cloud offers
many advanced features related to security and ensures that data is securely stored and handled.
Once the data is stored in the cloud, it is easier to get back
restore that data using the cloud.
Cloud applications improve collaboration by allowing groups of
people to quickly and easily share information in the cloud via shared storage.
Cloud allows us to quickly and easily access store information
Cloud computing reduces both hardware and software maintenance
Cloud computing allows us to easily access all cloud data via mobile.
-use model: Cloud computing offers Applicatio
7) Unlimited storage capacity: Cloud offers us a huge amount of storing capacity for storing our
important data such as documents, images, audio, video, etc. in one place.
Data security is one of the biggest advantages of cloud computing. Cloud offers
Once the data is stored in the cloud, it is easier to get back-up and
Cloud applications improve collaboration by allowing groups of
on in the cloud via shared storage.
quickly and easily access store information
Cloud computing reduces both hardware and software maintenance
Cloud computing allows us to easily access all cloud data via mobile.
Cloud computing offers Application Programming
Cloud offers us a huge amount of storing capacity for storing our
Data security is one of the biggest advantages of cloud computing. Cloud offers

Cloud Computing
DISADVANTAGES OF CLOUD COMPUTING
A list of the disadvantage of cloud computing is given below -
1) Internet Connectivity: As you know, in cloud computing, every data (image, audio, video,
etc.) is stored on the cloud, and we access these data through the cloud by using the internet
connection. If you do not have good internet connectivity, you cannot access these data.
However, we have no any other way to access data from the cloud.
2) Vendor lock-in: Vendor lock-in is the biggest disadvantage of cloud computing. Organizations
may face problems when transferring their services from one vendor to another. As different
vendors provide different platforms, that can cause difficulty moving from one cloud to another.
3) Limited Control: As we know, cloud infrastructure is completely owned, managed, and
monitored by the service provider, so the cloud users have less control over the function and
execution of services within a cloud infrastructure.
4) Security: Although cloud service providers implement the best security standards to store
important information. But, before adopting cloud technology, you should be aware that you will
be sending all your organization's sensitive information to a third party, i.e., a cloud computing
service provider. While sending the data on the cloud, there may be a chance that your
organization's information is hacked by Hackers.
VIRTUALIZATION IN CLOUD COMPUTING
Virtualization is the "creation of a virtual (rather than actual) version of something, such as a
server, a desktop, a storage device, an operating system or network resources".
In other words, Virtualization is a technique, which allows sharing a single physical instance of a
resource or an application among multiple customers and organizations. It does by assigning a
logical name to a physical storage and providing a pointer to that physical resource when
demanded.
What is the concept behind the Virtualization?
Creation of a virtual machine over existing operating system and hardware is known as
Hardware Virtualization. A Virtual machine provides an environment that is logically separated
from the underlying hardware.
The machine on which the virtual machine is going to create is known as Host Machine and that
virtual machine is referred as a Guest Machine.

Cloud Computing
Types of Virtualization:
1. Hardware Virtualization.
2. Operating system Virtualization.
3. Server Virtualization.
4. Storage Virtualization.
1) Hardware Virtualization:
When the virtual machine software or virtual machine manager (VMM) is directly installed on
the hardware system is known as hardware virtualization.
The main job of hypervisor is to control and monitoring the processor, memory and other
hardware resources.
After virtualization of hardware system we can install different operating system on it and run
different applications on those OS.
Usage:
Hardware virtualization is mainly done for the server platforms, because controlling virtual
machines is much easier than controlling a physical server.
2) Operating System Virtualization:
When the virtual machine software or virtual machine manager (VMM) is installed on the Host
operating system instead of directly on the hardware system is known as operating system
virtualization.
Usage:
Operating System Virtualization is mainly used for testing the applications on different platforms
of OS.
3) Server Virtualization:
When the virtual machine software or virtual machine manager (VMM) is directly installed on
the Server system is known as server virtualization.
Usage:

Cloud Computing
Server virtualization is done because a single physical server can be divided into multiple servers
on the demand basis and for balancing the load.
4) Storage Virtualization:
Storage virtualization is the process of grouping the physical storage from multiple network
storage devices so that it looks like a single storage device
Storage virtualization is also implemented by using software applications.
BUILDING CLOUD COMPUTING ENVIRONM
For building a dedicated cloud infrastructure
achieve it. Also, it is important to go for the best hosting providers for this since we have to
invest a good amount of resources in it.
 The creation of cloud computing environments encompasses both the development of
applications and systems that leverage cloud computing solutions and the creation of
frameworks, platforms, and infrastructures delivering cloud computing services.
o Application development
o Infrastructure and system development
o Computing platforms and technologies
APPLICATION DEVELOPMENT:
on the demand basis and for balancing the load.
process of grouping the physical storage from multiple network
storage devices so that it looks like a single storage device.
Storage virtualization is also implemented by using software applications.
BUILDING CLOUD COMPUTING ENVIRONMENTS
dedicated cloud infrastructure, there are several key requirements are needed to
invest a good amount of resources in it.
cloud computing environments encompasses both the development of
Application development
nfrastructure and system development
Computing platforms and technologies
APPLICATION DEVELOPMENT:
process of grouping the physical storage from multiple network
, there are several key requirements are needed to
cloud computing environments encompasses both the development of

Cloud Computing
 Applications that leverage cloud computing benefit from its capability to dynamically
scale on demand.
 One class of applications that takes the biggest advantage of this feature is that of Web
applications.
 Their performance is mostly influenced by the workload generated by varying user
demands.
 With the diffusion of Web2.0 technologies, the Web has become a platform for
developing rich and complex applications including enterprise applications that now
leverage the Internet as the preferred channel for service delivery and user interaction.
 These applications are characterized by complex processes that are triggered by the
interaction with users and develop through the interaction between several tiers behind
the Web front end.
 These are the applications that are mostly sensible to inappropriate sizing of
infrastructure and service deployment or variability in workload
 Another class of applications that can potentially gain considerable advantage by
leveraging cloud computing is represented by resource-intensive applications.
 These can be either data- intensive or compute-intensive applications.
 Resource-intensive applications are not interactive and they are mostly characterized by
batch processing.
 Cloud computing provides a solution for on-demand and dynamic scaling across the
entire stack of computing.
 This is achieved by
 providing methods for renting compute power, storage, and networking;
 (b) offering runtime environments designed for scalability and dynamic sizing; and
 (c) providing application services that mimic the behavior of desktop applications but that
are completely hosted and managed on the provider side.
 All these capabilities leverage service orientation, which allows a simple and seamless
integration into existing systems.
 Developers access such services via simple Web interfaces, often implemented through
representational state transfer (REST) Web services.

Cloud Computing
 These have become well-known abstractions, making the development and management
of cloud applications and systems practical and straightforward.
INFRASTRUCTURE AND SYSTEM DEVELOPMENT
 Distributed computing is a foundational model for cloud computing because cloud
systems are distributed systems.
 Besides administrative tasks mostly connected to the accessibility of resources in the
cloud, the extreme dynamism of cloud systems—where new nodes and services are
provisioned on demand—constitutes the major challenge for engineers and developers.
 This characteristic is pretty peculiar to cloud computing solutions and is mostly addressed
at the middleware layer of computing system.
 Infrastructure-as-a-Service solutions provide the capabilities to add and remove
resources, but it is up to those who deploy systems on this scalable infrastructure to make
use of such opportunities with wisdom and effectiveness.
 Platform-as-a-Service solutions embed into their core offering algorithms and rules that
control the provisioning process and the lease of resources.
 These can be either completely transparent to developers or subject to fine control.
 Integration between cloud resources and existing system deployment is another element
of concern.
 Web services have become the primary access point to cloud computing systems from a
programmatic standpoint.
 Therefore, service orientation is the underlying paradigm that defines the architecture of a
cloud computing system.
 Cloud computing is often summarized with the acronym XaaS—Everything-as-a-
Service—that clearly underlines the central role of service orientation.
 Despite the absence of a unique standard for accessing the resources serviced by different
cloud providers, the commonality of technology smoothes the learning curve and
simplifies the integration of cloud computing into existing systems.
 Virtualization is an other element that plays a fundamental role in cloud computing. This
technology is a core feature of the infrastructure used by cloud providers.
COMPUTING PLATFORM

Cloud Computing
 Development of a cloud computing application happens by leveraging platforms and
frameworks that provide different types of services, from the bare-metal infrastructure to
customizable applications serving specific purposes.
1. Amazon web services (AWS)
2. Google AppEngine
3. Microsoft Azure
4. Hadoop
5. Force.com and Salesforce.com
6. Manjrasoft Aneka
PRINCIPLES OF PARALLEL AND DISTRIBUTED COMPUTING
 Parallel computing refers to the process of executing several processors an application or
computation simultaneously.
 Generally, it is a kind of computing architecture where the large problems break into
independent, smaller, usually similar parts that can be processed in one go.
 It is done by multiple CPUs communicating via shared memory, which combines results
upon completion.
 It helps in performing large computations as it divides the large problem between more
than one processor.
 Parallel computing also helps in faster application processing and task resolution by
increasing the available computation power of systems.
 The parallel computing principles are used by most supercomputers employ to operate.
 The operational scenarios that need massive processing power or computation, generally,
parallel processing is commonly used there.
Parallel Computing: In parallel computing multiple processors performs multiple tasks
assigned to them simultaneously. Memory in parallel systems can either be shared or
distributed. Parallel computing provides concurrency and saves time and money.
Distributed Computing: In distributed computing we have multiple autonomous computers
which seems to the user as single system. In distributed systems there is no shared memory and
computers communicate with each other through message passing. In distributed computing a
single task is divided among different computers.

Cloud Computing
Difference between Parallel Computing and Distributed Computing:
S.NO Parallel Computing Distributed Computing
1. Many operations are performed
simultaneously
System components are located at different
locations
2. Single computer is required Uses multiple computers
3. Multiple processors perform
multiple operations
Multiple computers perform multiple operations
4. It may have shared or distributed
memory
It have only distributed memory
5. Processors communicate with each
other through bus
Computer communicate with each other through
message passing.
6. Improves the system performance Improves system scalability, fault tolerance and
resource sharing capabilities
HARDWARE ARCHITECTURE OF PARALLEL COMPUTING
 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
CLOUD COMPUTING PLATFORMS AND TECHNOLOGIES
Cloud computing applications develops by leveraging platforms and frameworks. Various types
of services are provided from the bare metal infrastructure to customize-able applications serving
specific purposes.
Amazon Web Services (AWS) – AWS provides different wide-ranging clouds IaaS services,
which ranges from virtual compute, storage, and networking to complete computing stacks.
AWS is well known for its storage and compute on demand services, named as Elastic
Compute Cloud (EC2) and Simple Storage Service (S3). EC2 offers customizable virtual
hardware to the end user which can be utilize as the base infrastructure for deploying
computing systems on the cloud. It is likely to choose from a large variety of virtual hardware
configurations including GPU and cluster instances. Either the AWS console, which is a wide-
ranged Web portal for retrieving AWS services, or the web services API available for several

Cloud Computing
programming language is used to deploy the EC2 instances. EC2 also offers the capability of
saving an explicit running instance as image, thus allowing users to create their own templates
for deploying system. S3 stores these templates and delivers persistent storage on demand. S3
is well ordered into buckets which contains objects that are stored in binary form and can be
grow with attributes. End users can store objects of any size, from basic file to full disk images
and have them retrieval from anywhere. In addition, EC2 and S3, a wide range of services can
be leveraged to build virtual computing system including: networking support, caching system,
DNS, database support, and others.
Google AppEngine – Google AppEngine is a scalable runtime environment frequently
dedicated to executing web applications. These utilize benefits of the large computing
infrastructure of Google to dynamically scale as per the demand. AppEngine offers both a
secure execution environment and a collection of which simplifies the development if scalable
and high-performance Web applications. These services include: in-memory caching, scalable
data store, job queues, messaging, and corn tasks. Developers and Engineers can build and test
applications on their own systems by using the AppEngine SDK, which replicates the
production runtime environment, and helps test and profile applications. On completion of
development, Developers can easily move their applications to AppEngine, set quotas to
containing the cost generated, and make it available to the world. Currently, the supported
programming languages are Python, Java, and Go.
Microsoft Azure – Microsoft Azure is a Cloud operating system and a platform in which user
can develop the applications in the cloud. Generally, a scalable runtime environment for web
applications and distributed applications is provided. Application in Azure are organized
around the fact of roles, which identify a distribution unit for applications and express the
application’s logic. Azure provides a set of additional services that complement application
execution such as support for storage, networking, caching, content delivery, and others.
Hadoop – Apache Hadoop is an open source framework that is appropriate for processing
large data sets on commodity hardware. Hadoop is an implementation of MapReduce, an
application programming model which is developed by Google. This model provides two
fundamental operations for data processing: map and reduce. Yahoo! Is the sponsor of the
Apache Hadoop project, and has put considerable effort in transforming the project to an
enterprise-ready cloud computing platform for data processing. Hadoop is an integral part of
the Yahoo! Cloud infrastructure and it supports many business processes of the corporates.
Currently, Yahoo! Manges the world’s largest Hadoop cluster, which is also available to
academic institutions.
Force.com and Salesforce.com – Force.com is a Cloud computing platform at which user can
develop social enterprise applications. The platform is the basis of SalesForce.com – a
Software-as-a-Service solution for customer relationship management. Force.com allows
creating applications by composing ready-to-use blocks: a complete set of components
supporting all the activities of an enterprise are available. From the design of the data layout to
the definition of business rules and user interface is provided by Force.com as a support. This

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platform is completely hostel in the Cloud, and provides complete access to its functionalities,
and those implemented in the hosted applications through Web services technologies.
PARALLEL COMPUTING AND DISTRIBUTED COMPUTING
Parallel Computing: In parallel computing multiple processors performs multiple tasks
assigned to them simultaneously. Memory in parallel systems can either be shared or
distributed. Parallel computing provides concurrency and saves time and money.
It is the use of multiple processing elements simultaneously for solving any problem. Problems
are broken down into instructions and are solved concurrently as each resource that has been
applied to work is working at the same time.
Advantages of Parallel Computing over Serial Computing are as follows:
1. It saves time and money as many resources working together will reduce the time and cut
potential costs.
2. It can be impractical to solve larger problems on Serial Computing.
3. It can take advantage of non-local resources when the local resources are finite.
4. Serial Computing ‘wastes’ the potential computing power, thus Parallel Computing makes
better work of the hardware.
Types of Parallelism:
1. Bit-level parallelism – It is the form of parallel computing which is based on the
increasing processor’s size. It reduces the number of instructions that the system must
execute in order to perform a task on large-sized data.
Example: Consider a scenario where an 8-bit processor must compute the sum of two 16-
bit integers. It must first sum up the 8 lower-order bits, then add the 8 higher-order bits,
thus requiring two instructions to perform the operation. A 16-bit processor can perform
the operation with just one instruction.
2. Instruction-level parallelism – A processor can only address less than one instruction for
each clock cycle phase. These instructions can be re-ordered and grouped which are later
on executed concurrently without affecting the result of the program. This is called
instruction-level parallelism.
3. Task Parallelism – Task parallelism employs the decomposition of a task into subtasks
and then allocating each of the subtasks for execution. The processors perform the
execution of sub-tasks concurrently.
4. Data-level parallelism (DLP) – Instructions from a single stream operate concurrently on
several data – Limited by non-regular data manipulation patterns and by memory bandwidth
Why parallel computing?
 The whole real-world runs in dynamic nature i.e. many things happen at a certain time but
at different places concurrently. This data is extensively huge to manage.
 Real-world data needs more dynamic simulation and modeling, and for achieving the same,
parallel computing is the key.
 Parallel computing provides concurrency and saves time and money.

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 Complex, large datasets, and their management can be organized only and only using
parallel computing’s approach.
 Ensures the effective utilization of the resources. The hardware is guaranteed to be used
effectively whereas in serial computation only some part of the hardware was used and the
rest rendered idle.
 Also, it is impractical to implement real-time systems using serial computing.
Applications of Parallel Computing:
 Databases and Data mining.
 Real-time simulation of systems.
 Science and Engineering.
 Advanced graphics, augmented reality, and virtual reality.
Limitations of Parallel Computing:
 It addresses such as communication and synchronization between multiple sub-tasks and
processes which is difficult to achieve.
 The algorithms must be managed in such a way that they can be handled in a parallel
mechanism.
 The algorithms or programs must have low coupling and high cohesion. But it’s difficult to
create such programs.
 More technically skilled and expert programmers can code a parallelism-based program
well.
Future of Parallel Computing: The computational graph has undergone a great transition
from serial computing to parallel computing. Tech giant such as Intel has already taken a step
towards parallel computing by employing multicore processors. Parallel computation will
revolutionize the way computers work in the future, for the better good. With all the world
connecting to each other even more than before, Parallel Computing does a better role in
helping us stay that way. With faster networks, distributed systems, and multi-processor
computers, it becomes even more necessary.
Distributed Computing: In distributed computing, we have multiple autonomous computers
which seems to the user as single system. In distributed systems there is no shared memory and
computers communicate with each other through message passing. In distributed computing a
single task is divided among different computers.
A Distributed System is a Network of Machines that can exchange information with each other
through Message-passing. It can be very useful as it helps in resource sharing.
 Client/Server Systems: Client requests to the server for resources or a task to do, the
server allocates the resource or performs the task and sends the result in the form of
response to the request of the client.
 Peer to Peer Systems: As nodes are an important part of a system. In this, each node
performs their own task on their local memory and share data through the supporting
medium, this node can work as a server or as a client for a system.

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 Middleware: It works as a base for different interoperability applications running on
different operating systems. Data can be transferred to other between others by using this
service.
 Three-tier: In this data of the client is stored in the middle tire rather than sorting into the
client system or on their server through which development can be done easily. This is
mostly used in web or online applications.
 N-tier: When interoperability sends the request to another application to perform a task or
to provide a service.
Types of Distributed Systems
A distributed system is also known as distributed computer science and distributed databases;
independent components that interact with other different machines that exchange messages to
achieve common goals. As such, the distributed system appears to the end-user like an
interface or a computer. Together the system can maximize resources and information while
preventing system failure and did not affect service availability.
1. Distributed Computing System:
This distributed system is used in performance computation which requires high computing.
 Cluster Computing: A collection of connected computers that works together as a unit to
perform operations together, functioning in a single system. Clusters are generally
connected quickly via local area networks & each node is running the same operating
system.

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When input comes from a client to the main computer, the master CPU divides the task into
simple jobs and send it to slaves note to do it when the jobs are done by the slave nodes, they
send back to the master node, and then it shows the result to the main computer.
Advantages:
 High Performance
 Easy to manage
 Scalable
 Expandability
 Availability
 Flexibility
Disadvantages:
 High cost
 The problem in finding fault
 More space is needed
Applications of Cluster Computing:
 In many web applications functionalities such as Security, Search Engines, Database
servers, web servers, proxy, and email.
 It is flexible to allocate works as small data tasks for processing.
 Assist and help to solve complex computational problems
 Cluster computing can be used in weather modeling
 Earthquake, Nuclear, Simulation, and tornado forecast
 Grid computing: In grid computing, the subgroup consists of distributed systems, which
are often set up as a network of computer systems, each system can belong to a different
administrative domain and can differ greatly in terms of hardware, software, and
implementation network technology.

Cloud Computing
The different department has a different computer with different OS to mage the control node
is present which help different computer with different OS to communicate with each other and
transfer messages to work.
Advantages:
 Can solve bigger and more complex problems in a shorter time frame. Easier collaboration
with other organizations and better use of existing equipment
Disadvantages:
 Grid software and standards continue to evolve
 Getting started learning curve
 Non-interactive job submission
 You may need a fast connection between computer resources.
 Licensing on many servers can be prohibitive for some applications.
Applications of Grid Computing
 Organizations that develop grid standards and practices for the guild line.
 Works as a middleware solution for connecting different businesses.
 It is a solution-based solution that can meet computing, data, and network needs.
2. Distributed transaction processing: It works across different servers using multiple
communication models. The four characteristics that transactions have:
 Atomic: the transaction taking place must be indivisible for the others
 Consistent: The transaction should be consistent after the transaction has been
done
 Isolated: A transaction must not interfere with another transaction
 Durable: Once an engaged transaction, the changes are permanent. Transactions
are often constructed as several sub-transactions, jointly forming a nested
transaction.

Cloud Computing
Each database can perform its own individual query containing data retrieval from two
different databases to give one single result
In the company’s middleware systems, the component that manages distributed (or nested)
transactions has formed the application integration core at the server or database. This was
referred to as the Transaction Processing Monitor (TP Monitor). Its main task was to allow an
application to access multiple servers/databases by providing a transactional programming
model. Many requests are sent to the database to get the result, to ensure each request gets
successfully executed and deliver result to each request, this work is handled by the TP
Monitor.
 Enterprise application integration: Enterprise Application Integration (EAI) is the
process of bringing different businesses together. The databases and workflows associated
with business applications ensure that the business uses information consistently and
changes in data done by one business application are reflected correctly in another’s. Many

Cloud Computing
organizations collect different data from different plate forms in the internal systems and
then they use those data are used in the Trading system /physical medium.
 RPC: Remote Procedure Calls (RPC), a software element that sends a request to every
other software element with the aid of using creating a nearby method name and retrieving
the data Which is now known as remote method invocation (RMI). An app can have a
different database for managing different data and then they can communicate to each other
on different platforms. Suppose, if you login into your android device and watching you’re
a video on YouTube then you go to your laptop open YouTube you can see the same video
is in your watch list. RPC and RMI have the disadvantage that sender and receiver must be
running at the time for communication.
Purposes:
 Targets the application rules and implements them in the EAI system so that even if one of
the lines of business applications is replaced by the application of another vendor.

Cloud Computing
 An EAI system can use a group of applications as a front end, provide only one, consistent
access interface to those applications, and protect users from learning how to use different
software packages.
3. Distributed Pervasive System:
Pervasive Computing is also abbreviated as ubiquitous (Changed and removed) computing and
it is the new step towards integrating everyday objects with microprocessors so that this
information can communicate. a computer system available anywhere in the company or as a
generally available consumer system that looks like that same everywhere with the same
functionality but that operates from computing power, storage, and locations across the globe.
 Home system: Nowadays many devices used in the home are being digital so that we can
control them from anywhere and effectively.
 Electronic health system: Nowadays smart medical wearable devices are also present
through which we can monitor our health regularly.

Cloud Computing
 Sensor network (IoT devices): Internet devices only send data to the client to act
according to the data send to the device.
 Before sensory devices only send and send data to the client but now, they can store and
process the data to manage it efficiently.

Cloud Computing
Difference between Parallel Computing and Distributed Computing:
S.NO Parallel Computing Distributed Computing
1. Many operations are performed
simultaneously
System components are located at different
locations
2. Single computer is required Uses multiple computers
3. Multiple processors perform
multiple operations
Multiple computers perform multiple operations
4. It may have shared or distributed
memory
It have only distributed memory
5. Processors communicate with each
other through bus
Computer communicate with each other through
message passing.
6. Improves the system performance Improves system scalability, fault tolerance and
resource sharing capabilities
HARDWARE ARCHITECTURES FOR PARALLEL PROCESSING APPROACHES TO
PARALLEL PROGRAMMING
In parallel computing we should know following terms.
1. Era of computing –
The two fundamental and dominant models of computing are sequential and parallel. The
sequential computing era began in the 1940s and the parallel (and distributed) computing
era followed it within a decade.
2. Computing –
So, now the question arises that what is Computing?

Cloud Computing
Computing is any goal-oriented activity requiring, benefiting from, or creating computers.
Computing includes designing, developing and building hardware and software systems;
designing a mathematical sequence of steps known as an algorithm; processing, structuring
and managing various kinds of information
3. Type of Computing –
Following are two types of computing :
1. Parallel computing
2. Distributed computing
Parallel computing –
As in this article, we are going to learn Parallel computing so 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.
As we learn what is parallel computing and there type now we are going more deeply on the
topic of the parallel computing and understand the concept of the hardware architecture of
parallel computing.
Hardware architecture of parallel computing –
The hardware architecture of parallel computing is disturbed along the following categories as
given below:
1. Single-Instruction, Single-Data (SISD) Systems
2. Single-Instruction, Multiple-Data (SIMD) Systems
3. Multiple-Instruction, Single-Data (MISD) Systems
4. Multiple-Instruction, Multiple-Data (MIMD) Systems
Refer to learn about the hardware architecture of parallel computing – Flynn’s taxonomy
Hardware computing –
Computer hardware is the collection of physical parts of a computer system. This includes the
computer case, monitor, keyboard, and mouse. It also includes all the parts inside the computer
case, such as the hard disk drive, motherboard, video card, and many others. Computer
hardware is what you can physically touch.
SISD
SISD stands for 'Single Instruction and Single Data Stream'. It represents the organization of a
single computer containing a control unit, a processor unit, and a memory unit.
Instructions are executed sequentially, and the system may or may not have internal parallel
processing capabilities.
Most conventional computers have SISD architecture like the traditional Von-Neumann
computers.

Cloud Computing
Parallel processing, in this case, may be achieved by means of multiple functional units or by
pipeline processing.
Where, CU = Control Unit, PE = Processing Element, M = Memory
Instructions are decoded by the Control Unit and then the Control Unit sends the instructions to
the processing units for execution.
Data Stream flows between the processors and memory bi-directionally.
Examples:
Older generation computers, minicomputers, and workstations
SIMD
SIMD stands for 'Single Instruction and Multiple Data Stream'. It represents an organization
that includes many processing units under the supervision of a common control unit.
All processors receive the same instruction from the control unit but operate on different items of
data.
The shared memory unit must contain multiple modules so that it can communicate with all the
processors simultaneously.

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SIMD is mainly dedicated to array processing machines. However, vector processors can also be
seen as a part of this group.
MISD
MISD stands for 'Multiple Instruction and Single Data stream'.
MISD structure is only of theoretical interest since no practical system has been constructed
using this organization.
In MISD, multiple processing units operate on one single-data stream. Each processing unit
operates on the data independently via separate instruction stream.
1. Where, M = Memory Modules, CU = Control Unit, P = Processor Units

Cloud Computing
MIMD
MIMD stands for 'Multiple Instruction and Multiple Data Stream'.
In this organization, all processors in a parallel computer can execute different instructions and
operate on various data at the same time.
In MIMD, each processor has a separate program and an instruction stream is generated from
each program.

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