CLOUD COMPUTING UNIT-1

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
Cloud Computing
[Elective-2]
IV B.Tech, SEMESTER-1
UNIT-1
Dr.K.Venkata Subba Reddy, Professor and HOD, Department of CSE, KHIT, Guntur
KALLAM HARANADHAREDDY INSTITUTE OF TECHNOLOGY, GUNTUR
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COURSE OUTCOMES
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING 2/112
COURSE
COURSE
CODE
COURSE OUTCOMES
Cloud
Computing
[Elective-2]
C406.1
Describe system models, software environments for distributed and
cloud computing
C406.2 Analyze virtualization mechanism for datacenter automation
C406.3
Demonstrate knowledge on cloud architectural design, service models
and cloud security
C406.4 Construct cloud based software applications on top of cloud platforms
C406.5
Understand Mechanisms for Cloud Resource Management
Applications
C406.6 Learn the privacy policy of cloud providers

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UNIT -I:
Systems modeling, Clustering and virtualization
1.1 Scalable Computing over the Internet
1.1.1 The Age of Internet Computing
1.1.2 Scalable Computing Trends and New Paradigms
1.1.3 The Internet of Things and Cyber-Physical Systems
1.2 Technologies for Network-Based Systems
1.2.1 Multicore CPUs and Multithreading Technologies
1.2.2 GPU Computing to Exascale and Beyond.
1.2.3 Memory, Storage, and Wide-Area Networking
1.2.4 Virtual Machines and Virtualization Middleware
1.2.5 Data Center Virtualization for Cloud Computing
1.3 System Models for Distributed and Cloud Computing
1.3.1 Clusters of Cooperative Computers
1.3.2 Grid Computing Infrastructures
1.3.3 Peer-to-Peer Network Families
1.3.4 Cloud Computing over the Internet
1.4 Software Environments for Distributed Systems and
Clouds
1.4.1 Service-Oriented Architecture (SOA)
1.4.2 Trends toward Distributed Operating Systems
1.4.3 Parallel and Distributed Programming Models
1.5 Performance, Security, and Energy Efficiency
1.5.1 Performance Metrics and Scalability Analysis .
1.5.2 Fault Tolerance and System Availability
1.5.3 Network Threats and Data Integrity
1.5.4 Energy Efficiency in Distributed Computing
TOPICS TO BE COVERED IN UNIT-I

UNIT-I
UNIT -I: Systems modeling, Clustering and virtualization
 Scalable Computing over the Internet
 Technologies for Network based systems
 System models for Distributed and Cloud Computing,
 Software environments for distributed systems and clouds
 Performance
 Security And Energy Efficiency
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1.SCALABLE COMPUTING OVER THE INTERNET
Scalable Computing over the Internet
 1.1 The Age of Internet Computing
 1.2 Scalable Computing Trends and New Paradigms
 1.3 The Internet of Things and Cyber-Physical Systems
1.1. The Age of Internet Computing
High-performance computing (HPC)
 applications is no longer optimal for measuring system performance
High-throughput computing (HTC) systems
 built with parallel and distributed computing technologies
 upgrade data centers using fast servers, storage systems, and high-bandwidth networks.
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SCALABLE COMPUTING OVER THE INTERNET
Computer technology has gone through five generations of development
1950 to 1970: Mainframes, including the IBM 360 and CDC 6400, were built to satisfy the demands
of large businesses and government organizations.
1960 to 1980: lower-cost mini computers such as the DEC PDP 11 and VAX Series became popular
among small businesses and on college campuses
1970 to 1990: widespread use of personal computers built with VLSI microprocessors
1980 to 2000: Massive numbers of portable computers and pervasive devices appeared in
both wired and wireless applications
Since1990: The use of both HPC and HTC systems hidden in clusters, grids, or Internet clouds has
proliferated
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 On the HTC side, peer-to-peer (P2P)
networks are formed for distributed file
sharing.
 A P2P system is built over many client
machines . Peer machines are globally
distributed in nature.
 P2P, cloud computing, and web service
platforms are more focused on HTC
applications than on HPC applications.
 Clustering and P2P technologies lead to the
development of computational grids or data
grids.
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On the HPC side
 supercomputers (massively parallel processors or MPPs) are gradually replaced by clusters of
cooperative computers out of a desire to share computing resources.
The cluster is often a collection
of homogeneous compute nodes that are physically connected in close range to one another.
Figure: Evolutionary trend toward parallel, distributed, and cloud
The maturity of radio-frequency identification
(RFID), Global Positioning System (GPS), and sensor
technologies has triggered the development of the
Internet of Things (IoT).

Distributed Computing is the opposite of centralized computing.
Parallel computing overlaps with distributed computing to a great extent.
Cloud computing overlaps with distributed, centralized, and parallel computing
Centralized computing
This is a computing paradigm by which all computer resources are centralized in one physical
system. All resources (processors, memory, and storage) are fully shared and tightly coupled
within one integrated OS. Many data centers and supercomputers are centralized systems, but
they are used in parallel, distributed, and cloud computing applications
Parallel computing
In parallel computing, all processors are either tightly coupled with centralized shared memory or
loosely coupled with distributed memory. Some authors refer to this discipline as parallel
processing. Inter processor communication is accomplished through shared memory or via
message passing. A computer system capable of parallel computing is commonly known as a
parallel computer [28]. Programs running in a parallel computer are called parallel programs. The
process of writing parallel programs is often referred to as parallel programming
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Distributed Computing is the opposite of centralized computing.
Parallel computing overlaps with distributed computing to a great extent.
Cloud computing overlaps with distributed, centralized, and parallel computing
Distributed computing:
This is a field of computer science/engineering that studies distributed systems. A distributed
system consists of multiple autonomous computers, each having its own private memory,
communicating through a computer network. Information exchange in a distributed system is
accomplished through message passing. A computer program that runs in a distributed system is
known as a distributed program. The process of writing distributed programs is referred to as .
Cloud computing
An Internet cloud of resources can be either a centralized or a distributed computing system. The
cloud applies parallel or distributed computing, or both. Clouds can be built with physical or
virtualized resources over large data centers that are centralized or distributed. Some authors
consider cloud computing to be a form of utility computing or service computing distributed
programming
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 In the future, both HPC and HTC systems will demand multi core or many-core processors that
can handle large numbers of computing threads per core.
 Both HPC and HTC systems emphasize parallelism and distributed computing. Future HPC and
HTC systems must be able to satisfy this huge demand in computing power in terms of
throughput, efficiency, scalability, and reliability.
 The system efficiency is decided by speed, programming, and energy factors. Meeting these goals
requires to yield the following design objectives:
 Efficiency
 Dependability
 Adaptation in the programming model
 Flexibility in application deployment
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1.2. SCALABLE COMPUTING TRENDS AND NEW PARADIGMS
1.2.1.Innovative Applications
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1.2.2.TREND TOWARDS UTILITY COMPUTING
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The vision of computer utilities in modern distributed computing systems.

 Internet of Things is about connecting "Things" ( Objects and
Machines) to the internet and eventually to each other;
 Cyber Physical Systems (CPS) are integration of computation,
networking and physical process.
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1.3.THE INTERNET OF THINGS AND CYBER-PHYSICAL SYSTEMS

2.TECHNOLOGIES FOR NETWORK-BASED SYSTEMS
Technologies for Network-Based Systems
 2.1 Multicore CPUs and Multithreading Technologies
 2.2 GPU Computing to Exascale and Beyond
 2.3 Memory, Storage, and Wide-Area Networking
 2.4 Virtual Machines and Virtualization Middleware
 2.5Data Center Virtualization for Cloud Computing
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MULTITHREADING
TECHNOLOGY
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Five independent threads of
instructions to four pipelined data
paths in each of the following five
processor categories
2.1 MULTICORE CPUS AND MULTITHREADING TECHNOLOGIES

2.2 GPU COMPUTING TO EXASCALE AND BEYOND
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 GPU was marketed by NVIDIA in 1999
 Unlike CPUs, GPUs have a throughput architecture that exploits massive parallelism by
executing many concurrent threads slowly, instead of executing a single long thread in a
conventional microprocessor very quickly.

2.3.MEMORY STORAGE AND WIDE AREA NETWORKS
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LAN typically is used to connect client hosts
to big servers
A storage area network (SAN) connects
servers to network storage such as disk
arrays
Network attached storage (NAS) connects
client hosts directly to the disk arrays.

2.4 VIRTUAL MACHINES AND VIRTUALIZATION MIDDLEWARE
 Virtual machines (VMs) offer novel solutions to underutilized resources, application inflexibility,
software manageability, and security concerns in existing physical machines.
 To build large clusters, grids, and clouds, we need to access large amounts of computing, storage,
and networking resources in a virtualized manner
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Compare Physical Machine(a) with Three Virtual Machines (b,c,d)
Multiple OS environments can exist
simultaneously on the same machine
is called Virtual Machine
If you want to run multiple operating
systems on one machine, or multiple
copies of the same operating system,
then you only have two ways to do
it: dual boot or virtual machine

VIRTUAL MACHINES AND VIRTUALIZATION MIDDLEWARE
 a native VM installed with the use of a VMM called a hypervisor in privileged mode The guest
OS could be a Linux system and the hypervisor is the server system
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HOSTED VIRTUAL MACHINE NATIVE VIRTUAL MACHINE

2.5.DATA CENTER VIRTUALIZATION FOR CLOUD COMPUTING
 A large data center may be built with thousands of servers.
 Smaller data centers are typically built with hundreds of servers.
 High-end switches or routers may be too cost-prohibitive for
building data centers
 Currently, nearly all cloud computing data centers use Ethernet
as their fundamental network technology
 30% of data center cost: IT Equipment Servers/Disks
 33% of data center cost for chillers
 18% of data center cost for UPS
 9% of data center cost for A.C
 7% of data center cost for lighting in room
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3.SYSTEM MODELS FOR DISTRIBUTED AND CLOUD COMPUTING
System Models for Distributed and Cloud Computing
 3.1 Clusters of Cooperative Computers
 3.2 Grid Computing
 3.3 Peer-to-Peer Network
 3.4 Cloud Computing over the Internet
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3.1 Clusters of Cooperative Computers
Clustering means
that multiple
servers are
grouped together
to achieve the
same service
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3.2 Grid Computing
Grid Computing is a subset of distributed computing
Grid Computing is a technique in which the idle systems in the
Network and their “ wasted “ CPU cycles can be efficiently used
by uniting pools of servers, storage systems and networks
into a single large virtual system for resource sharing
dynamically at runtime.
Example : A small LAN that consists of around 20 systems out of which 10 systems are
idle and 5 systems are using less amount of CPU and their CPU cycles are wasted.
Now grid computing efficiently utilize those “wasted CPU cycles” into “working
cycles”.
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Where Grid Computing is used ?
 Telecommunication Organizations
 Government Offices
 Multinational Companies
 Financial Organizations
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Advantages of Grid computing
 It can solve larger and complex problems in a short time.
 No computer be idle in this system.
 Unit works makes all computer like a super computer.
 Make better uses of hardware.
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3.3 Peer-to-Peer Network
 In a P2P network, the "peers" are computer systems which are
connected to each other via the Internet.
 Files can be shared directly between systems on
the network without the need of a central server.
 In other words, each computer on a P2P network becomes a file
server as well as a client
 when you create an ad-hoc network between two computers,
you create a peer-to-peer network between them
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Peer-to-Peer Network
Key advantages of a P2P network
 Easy file sharing: An advanced P2P network can share files quickly over large distances.
 Reduced costs: There is no need to invest in a separate computer for a server when setting
up a P2P network.
 Adaptability: P2P network extends to include new clients easily.
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3.4 Cloud Computing over the Internet
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Infrastructure as a Service (IaaS)
servers, storage, networks, and the data center
Platform as a Service (PaaS)
Middleware, databases, development tools, and
some runtime support such as Web 2.0 and Java
Software as a Service (SaaS)
Applied to Business, Industry applicaions,
ERP,HR,CRM

Cloud Computing over the Internet
Internet clouds offer Three deployment modes:
 Private Cloud
 Public Cloud
 Hybrid Cloud
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4 Software Environments for Distributed Systems and Clouds
4.1 Service-Oriented Architecture (SOA)
4.2 Trends toward Distributed Operating Systems
4.3 Parallel and Distributed Programming Models
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Software Environments for Distributed Systems and Clouds
Popular software environments for using distributed and cloud
computing systems
 Service-Oriented Architecture (SOA)
 Layered Architecture In web services
 Java RMI and CORBA
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Service-oriented architecture (SOA)
A collection of services. These services communicate with each
other
 SOA applies to building grids, clouds, grids of clouds, clouds of
grids, clouds of clouds
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Software Environments for Distributed Systems and Clouds

Layered Architecture In web services
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4.2 Trends toward Distributed Operating Systems
 One interface to all resources in the
network
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4.3 Parallel and Distributed Programming Models
Message Passing Interface
MapReduce
MapReduce is a processing technique and a program model for distributed
computing based on java. The MapReduce algorithm contains two
important tasks, namely Map and Reduce. Map takes a set of data and
converts it into another set of data, where individual elements are broken
down into tuples.
Hadoop Library
Hadoop software library is a framework that allows for the distributed
processing of large data sets across clusters of computers using simple
programming models
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5. Performance, Security, and Energy Efficiency
5.1 Performance Metrics and Scalability Analysis .
5.2 Fault Tolerance and System Availability
5.3 Network Threats and Data Integrity
5.4 Energy Efficiency in Distributed Computing
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 Performance metrics are needed to measure various distributed
systems
 Measure CPU speed in MIPS and network bandwidth in Mbps
Dimensions of Scalability
 Size scalability
 Software scalability
 Application scalability
 Technology scalability
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5 .1.Performance Metrics and Scalability Analysis

5.2 Fault Tolerance and System Availability
Fault tolerance is the property that enables a system to continue
operating properly in the event of the failure some of its
components.
System Availability =MTTF/MTTF +MTTR
mean time to failure (MTTF)
mean time to repair(MTTR)
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5.3 Network Threats and Data Integrity
Three security requirements are often
considered:
confidentiality,
integrity,
availability
for most Internet service providers
and cloud users.
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5.4.Energy Efficiency in Distributed Computing
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The middleware layer acts as a
bridge between the application
layer and the resource layer.
This layer provides resource
broker, communication service,
task analyzer, task scheduler,
security access, reliability control,
and information service
The resource layer consists of a
wide range of resources including
computing nodes and storage
units.

CLOUD COMPUTING UNIT-1

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