Course Name : Cloud Computing �Code : 20CS24�UNIT-I

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UNIT I:

Systems Modeling, Clustering and Virtualization: Scalable Computing over the Internet-The Age of Internet Computing, Scalable computing over the internet, Technologies for Network Based Systems, System models for Distributed and Cloud Computing, Performance, Security and Energy Efficiency.

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Cloud Computing

• Cloud Computing provides us a means by which we can access the applications as utilities, over the Internet. It allows us to create, configure, and customize applications online.

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What is Cloud?

• The term Cloud refers to a Network or Internet. In other words, we can say that Cloud is something, which is present at remote location. Cloud can provide services over network, i.e., on public networks or on private networks, i.e., WAN, LAN or VPN.
• Applications such as e-mail, web conferencing, customer relationship management (CRM),all run in cloud.

What is Cloud Computing?

• Cloud Computing refers to manipulating, configuring, and accessing the applications online. It offers online data storage, infrastructure and application.

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Data Deluge Enabling New Challenges

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1.Scalable Computing over the Internet

• The Age of Internet Computing
• Scalable Computing Trends and New Paradigms
• The Internet of Things and Cyber-Physical Systems

The Age of Internet Computing

• The Platform Evolution
• High-Performance Computing
• High-Throughput Computing
• Three New Computing Paradigms
• Computing Paradigm Distinctions
• Distributed System Families

From Desktop/HPC/Grids to Internet Clouds in 30 Years

• HPC moving from centralized supercomputers to geographically distributed desktops, desksides, clusters, and grids to clouds over last 30 years.
• R/D efforts on HPC, clusters, Grids, P2P, and virtual machines has laid the foundation of cloud computing that has been greatly advocated since 2007.
• Location of computing infrastructure in areas with lower costs in hardware, software, datasets, space, and power requirements – moving from desktop computing to datacenter-based clouds.

Interactions among 4 technical challenges:�Data Deluge, Cloud Technology, eScience and Multicore/Parallel Computing

Clouds and Internet of Things

HPC: High-Performance Computing

HTC: High-Throughput Computing

P2P: �Peer to Peer

MPP: �Massively Parallel Processors

Three New Computing Paradigms

• Radio-frequency identification (RFID)

Radio-frequency identification (RFID) uses electromagnetic fields to automatically identify and track tags attached to objects. The tags contain electronically stored information.

• Global Positioning System (GPS)

The Global Positioning System (GPS), originally NavstarGPS, is a space-based radionavigation system owned by the United States government and operated by the United States Air Force. It is a global navigation satellite system that provides geolocation and time information to a GPS receiver anywhere on or near the Earth where there is an unobstructed line of sight to four or more GPS satellites

• Internet of Things (IoT)

The Internet of things (IoT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to connect and exchange data.

Computing Paradigm Distinctions

• Centralized Computing
• All computer resources are centralized in one physical system.
• Parallel Computing
• All processors are either tightly coupled with central shard memory or loosely coupled with distributed memory
• Distributed Computing
• Field of CS/CE that studies distributed systems. A distributed system consists of multiple autonomous computers, each with its own private memory, communicating over a network.
• Cloud Computing
• An Internet cloud of resources that may be either centralized or decentralized. The cloud apples to parallel or distributed computing or both. Clouds may be built from physical or virtualized resources.

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Distributed System Families

• Networks and networks of clusters have been consolidated into many national projects designed to establish wide area computing infrastructures, known as computational grids or data grids.
• In the future, both HPC and HTC systems will demand multicore 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.
• Meeting these goals requires to yield the following design objectives:
• Efficiency
• Dependability
• Adaptation in the programming model
• Flexibility in application deployment

Scalable Computing Trends and New Paradigms

• Degrees of Parallelism
• Bit-level parallelism (BLP)
• Instruction-level parallelism (ILP)
• VLIW (very long instruction word)
• Data-level parallelism (DLP)
• task-level parallelism (TLP)
• job-level parallelism (JLP)
• Innovative Applications
• The Trend toward Utility Computing
• The Hype Cycle of New Technologies

Innovative Applications

Technology Convergence toward HPC for Science and HTC for Business: Utility Computing

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2011 Gartner “IT Hype Cycle” for Emerging Technologies

Copyright © 2012, Elsevier Inc. All rights reserved.

The Internet of Things and Cyber-Physical Systems

• The Internet of Things

The Internet of Things (IoT) refers to a network of physical objects that are embedded with sensors, software, and other technologies to connect and exchange data with other devices and systems over the internet.

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• Cyber-Physical Systems(CPS)

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A cyber-physical system (CPS) is the result of interaction between computational processes and the physical world. A CPS integrates “cyber” (heterogeneous, asynchronous) with “physical” (concurrent and information-dense) objects. A CPS merges the “3C” technologies of computation, communication, and control

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2 TECHNOLOGIES FOR NETWORK-BASED SYSTEMS

2.1 Multicore CPUs and Multithreading Technologies

• 2.1.1 Advances in CPU Processors
• 2.1.2 Multicore CPU and Many-Core GPU Architectures
• 2.1.3 Multithreading Technology

33 year Improvement in Processor and Network Technologies

Advances in CPU Processors

Modern Multi-core CPU Chip

Multi-threading Processors

Four-issue

Superscalar processor (e.g. Sun Ultrasparc I)

• Implements instruction level parallelism (ILP) within a single processor.
• Executes more than one instruction during a clock cycle by sending multiple instructions to redundant functional units.

Fine-grain multithreaded processor

• Switch threads after each cycle
• Interleave instruction execution
• If one thread stalls, others are executed

Coarse-grain multithreaded processor

• Executes a single thread until it reaches certain situations

Simultaneous multithread processor (SMT)

• Instructions from more than one thread can execute in any given pipeline stage at a time.

5 Micro-architectures of CPUs

Each row represents the issue slots for a single execution cycle:

• A filled box indicates that the processor found an instruction to execute in that issue slot on that cycle;
• An empty box denotes an unused slot.

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2.2 GPU Computing to Exascale and Beyond

• 2.2.1 How GPUs Work
• 2.2.2 GPU Programming Model
• 2.2.3 Power Efficiency of the GPU

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How GPUs Work

• Early GPUs functioned as coprocessors attached to the CPU. Today, the NVIDIA GPU has been upgraded to 128 cores on a single chip. Furthermore, each core on a GPU can handle eight threads of instructions. This translates to having up to 1,024 threads executed concurrently on a single GPU.

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• Modern GPUs are not restricted to accelerated graphics or video coding. They are used in HPC systems to power supercomputers with massive parallelism at multicore and multithreading levels. GPUs are designed to handle large numbers of floating-point operations in parallel.

Architecture of A Many-Core Multiprocessor GPU interacting �with a CPU Processor

GPU Programming Model

The interaction between a CPU and GPU in performing parallel execution of floating-point operations concurrently. The CPU is the conventional multicore processor with limited parallelism to exploit.

Copyright © 2012, Elsevier Inc. All rights reserved.

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Example : NVIDIA Fermi GPU

GPU Performance: Power Efficiency of the GPU

Bottom – CPU - 0.8 Gflops/W/Core (2011)

Middle – GPU - 5 Gflops/W/Core (2011)

Top - EF – Exascale computing (10^18 Flops)

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1.2.3 Memory, Storage, and Wide-Area Networking

• 1.2.3.1 Memory Technology
• 1.2.3.2 Disks and Storage Technology
• 1.2.3.3 System-Area Interconnects
• 1.2.3.4 Wide-Area Networking

33 year Improvement in Memory and Disk Technologies

Copyright © 2012, Elsevier Inc. All rights reserved.

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Interconnection Networks

• SAN (storage area network) - connects servers with disk arrays
• LAN (local area network) – connects clients, hosts, and servers
• NAS (network attached storage) – connects clients with large storage systems

Virtual Machines and Virtualization Middleware

• Virtual Machines
• VM Primitive Operations
• Virtual Infrastructures

Initial Hardware Model

• All applications access hardware resources (i.e. memory, i/o) through system calls to operating system (privileged instructions)

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• Advantages
• Design is decoupled (i.e. OS people can develop OS separate of Hardware people developing hardware)
• Hardware and software can be upgraded without notifying the Application programs
• Disadvantage
• Application compiled on one ISA will not run on another ISA..
• Applications compiled for Mac use different operating system calls then application designed for windows.
• ISA’s must support old software
• Can often be inhibiting in terms of performance
• Since software is developed separately from hardware… Software is not necessarily optimized for hardware.

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Virtual Machines

A conventional computer has a single OS image. This offers a rigid architecture that tightly couples application software to a specific hardware platform. Some software running well on one machine may not be executable on another platform with a different instruction set under a fixed OS.

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Virtual machines (VMs) offer novel solutions to underutilized resources, application inflexibility, software manageability, and security concerns in existing physical machines.

Virtual Machines

• Eliminate real machine constraint
• Increases portability and flexibility
• Virtual machine adds software to a physical machine to give it the appearance of a different platform or multiple platforms.
• Benefits
• Cross platform compatibility
• Increase Security
• Enhance Performance
• Simplify software migration

Virtual Machine Basics

• Virtual software placed between underlying machine and conventional software
• Conventional software sees different ISA from the one supported by the hardware

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• Virtualization process involves:
• Mapping of virtual resources (registers and memory) to real hardware resources
• Using real machine instructions to carry out the actions specified by the virtual machine instructions

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VM Primitive Operations

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Virtual Infrastructures

• Virtual infrastructure is what connects resources to distributed applications. It is a dynamic mapping of system resources to specific applications. The result is decreased costs and increased efficiency and responsiveness.

Data Center Virtualization for Cloud Computing

• Data Center Growth and Cost Breakdown
• Low-Cost Design Philosophy
• Convergence of Technologies

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Convergence of Technologies

cloud computing is enabled by the convergence of technologies in four areas:

1. hardware virtualization and multi-core chips,
2. utility and grid computing,
3. SOA, Web 2.0, and WSmashups, and
4. Atonomic computing and data center automation

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System Models for Distributed and Cloud Computing

System Models for Distributed and Cloud Computing

• Clusters of Cooperative Computers
• Grid Computing Infrastructures
• Peer-to-Peer Network Families
• Cloud Computing over the Internet

Clusters of Cooperative Computers

• Cluster Architecture
• Single-System Image
• Hardware, Software, and Middleware Support
• Major Cluster Design Issues

A Typical Cluster Architecture

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Major Cluster Design Issues

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Grid Computing Infrastructures

• Computational Grids
• Grid Families

Computational or Data Grid

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Peer-to-Peer Network Families

• P2P Systems
• Overlay Networks
• P2P Application Families
• P2P Computing Challenges

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Peer-to-Peer (P2P) Network

• A distributed system architecture
• Each computer in the network can act as a client or server for other network computers.
• No centralized control
• Typically many nodes, but unreliable and heterogeneous
• Nodes are symmetric in function
• Take advantage of distributed, shared resources (bandwidth, CPU, storage) on peer-nodes
• Fault-tolerant, self-organizing
• Operate in dynamic environment, frequent join and leave is the norm

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Peer-to-Peer (P2P) Network

Overlay network - computer network built on top of another network.

• Nodes in the overlay can be thought of as being connected by virtual or logical links, each of which corresponds to a path, perhaps through many physical links, in the underlying network.

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• For example, distributed systems such as cloud computing, peer-to-peer networks, and client-server applications are overlay networks because their nodes run on top of the Internet.

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There are two types of overlay networks:

unstructured and structured.

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An unstructured overlay network is characterized by a random graph. There is no fixed route to send messages or files among the nodes. Often, flooding is applied to send a query to all nodes in an unstructured overlay, thus resulting in heavy network traffic and nondeterministic search results.

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Structured overlay networks follow certain connectivity topology and rules for inserting and removing nodes (peer IDs) from the overlay graph. Routing mechanisms are developed to take advantage of the structured overlays.

P2P Application Families

Cloud Computing over the Internet

• Internet Clouds
• The Cloud Landscape

The Cloud

• Historical roots in today’s �Internet apps
• Search, email, social networks
• File storage (Live Mesh, Mobile�Me, Flicker, …)
• A cloud infrastructure provides a framework to manage scalable, reliable, on-demand access to applications
• A cloud is the “invisible” backend to many of our mobile applications
• A model of computation and data storage based on “pay as you go” access to “unlimited” remote data center capabilities

Basic Concept of Internet Clouds

• Cloud computing is the use of computing resources (hardware and software) that are delivered as a service over a network (typically the Internet).
• The name comes from the use of a cloud-shaped symbol as an abstraction for the complex infrastructure it contains in system diagrams.
• Cloud computing entrusts remote services with a user's data, software and computation.

The Cloud Landscape

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The Next Revolution in IT�Cloud Computing

• Classical Computing
• Buy & Own
• Hardware, System Software, Applications often to meet peak needs.
• Install, Configure, Test, Verify, Evaluate
• Manage
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• Finally, use it
• $$$$....$(High CapEx)

• Cloud Computing
• Subscribe
• Use

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• $ - pay for what you use, based on QoS

Cloud Service Models (1)

Infrastructure as a service (IaaS)

• Most basic cloud service model
• Cloud providers offer computers, as physical or more often as virtual machines, and other resources.
• Virtual machines are run as guests by a hypervisor, such as Xen or KVM.
• Cloud users deploy their applications by then installing operating system images on the machines as well as their application software.
• Cloud providers typically bill IaaS services on a utility computing basis, that is, cost will reflect the amount of resources allocated and consumed.
• Examples of IaaS include: Amazon CloudFormation (and underlying services such as Amazon EC2), Rackspace Cloud, Terremark, and Google Compute Engine.

Cloud Service Models (2)

Platform as a service (PaaS)

• Cloud providers deliver a computing platform typically including operating system, programming language execution environment, database, and web server.
• Application developers develop and run their software on a cloud platform without the cost and complexity of buying and managing the underlying hardware and software layers.
• Examples of PaaS include: Amazon Elastic Beanstalk, Cloud Foundry, Heroku, Force.com, EngineYard, Mendix, Google App Engine, Microsoft Azure and OrangeScape.

Cloud Service Models (3)

Software as a service (SaaS)

• Cloud providers install and operate application software in the cloud and cloud users access the software from cloud clients.
• The pricing model for SaaS applications is typically a monthly or yearly flat fee per user, so price is scalable and adjustable if users are added or removed at any point.
• Examples of SaaS include: Google Apps, innkeypos, Quickbooks Online, Limelight Video Platform, Salesforce.com, and Microsoft Office 365.

SOFTWARE ENVIRONMENTS FOR DISTRIBUTED SYSTEMS AND CLOUDS

• Service-Oriented Architecture (SOA)
• Trends toward Distributed Operating Systems
• Parallel and Distributed Programming Models

Service-Oriented Architecture (SOA)

• Layered Architecture for Web Services and Grids
• Web Services and Tools
• The Evolution of SOA
• Grids versus Clouds

Service-oriented architecture (SOA)

• SOA is an evolution of distributed computing based on the request/reply design paradigm for synchronous and asynchronous applications.
• An application's business logic or individual functions are modularized and presented as services for consumer/client applications.
• Key to these services - their loosely coupled nature;
• i.e., the service interface is independent of the implementation.
• Application developers or system integrators can build applications by composing one or more services without knowing the services' underlying implementations.
• For example, a service can be implemented either in .Net or J2EE, and the application consuming the service can be on a different platform or language.

SOA key characteristics:

• SOA services have self-describing interfaces in platform-independent XML documents.
• Web Services Description Language (WSDL) is the standard used to describe the services.
• SOA services communicate with messages formally defined via XML Schema (also called XSD).
• Communication among consumers and providers or services typically happens in heterogeneous environments, with little or no knowledge about the provider.
• Messages between services can be viewed as key business documents processed in an enterprise.
• SOA services are maintained in the enterprise by a registry that acts as a directory listing.
• Applications can look up the services in the registry and invoke the service.
• Universal Description, Definition, and Integration (UDDI) is the standard used for service registry.
• Each SOA service has a quality of service (QoS) associated with it.
• Some of the key QoS elements are security requirements, such as authentication and authorization, reliable messaging, and policies regarding who can invoke services.

Layered Architecture for Web Services

Web Services and Tools

REST systems(Representational state transfer)

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SOAP(Simple Object Access Protocol)

The Evolution of SOA

Trends toward Distributed Operating Systems

• Distributed Operating Systems
• Amoeba versus DCE
• MOSIX2 for Linux Clusters
• Transparency in Programming Environments

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Transparent Cloud Computing Environment

Separates user data, application, OS, and space – good for cloud computing.

Parallel and Distributed Programming Models

• Message-Passing Interface (MPI)
• MapReduce
• Hadoop Library
• Open Grid Services Architecture (OGSA)
• Globus Toolkits and Extensions

Parallel and Distributed Programming

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Grid Standards and Middleware :

PERFORMANCE, SECURITY, AND ENERGY EFFICIENCY

• Performance Metrics and Scalability Analysis
• Fault Tolerance and System Availability
• Network Threats and Data Integrity
• Energy Efficiency in Distributed Computing

Performance Metrics and Scalability Analysis

• Performance Metrics
• Dimensions of Scalability
• Scalability versus OS Image Count
• Amdahl’s Law
• Problem with Fixed Workload
• Gustafson’s Law

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Dimensions of Scalability

• Size – increasing performance by increasing machine size
• Software – upgrade to OS, libraries, new apps.
• Application – matching problem size with machine size
• Technology – adapting system to new technologies

System Scalability vs. OS Multiplicity

Amdahl’s Law

• Consider the execution of a given program on a uniprocessor workstation with a total execution time of T minutes. Now, let’s say the program has been parallelized or partitioned for parallel execution on a cluster of many processing nodes. Assume that a fraction α of the code must be executed sequentially, called the sequential bottleneck. Therefore, (1 − α) of the code can be compiled for parallel execution by n processors. The total execution time of the program is calculated by α T + (1− α)T/n, where the first term is the sequential execution time on a single processor and the second term is the parallel execution time on n processing nodes. All system or communication overhead is ignored here. The I/O time or exception handling time is also not included in the following speedup analysis.
• Amdahl’s Law states that the speedup factor of using the n-processor system over the use of a single processor is expressed by:

Speedup =S= T/[αT +(1− α)T/n] =1/[α +(1 −α)/n]

Gustafson’s Law

• Let W be the workload in a given program. When using an n-processor system, the user scales the workload to

W′ = αW + (1 − α)nW

• Note that only the parallelizable portion of the workload is scaled n times in the second term. This scaled workload W′ is essentially the sequential execution time on a single processor. The parallel execution time of a scaled workload W′ on n processors is defined by a scaled-workload speedup as follows:

S′ =W′/ W =[αW + (1− α)nW]W = α+ (1 −α)n

• This speedup is known as Gustafson’s law. By fixing the parallel execution time at level W, the following efficiency expression is obtained:

E′ =S′/n= α/n+(1− α)

Fault Tolerance and System Availability

• HA (high availability) is desired in all clusters, grids, P2P networks, and cloud systems. A system is highly available if it has a long mean time to failure (MTTF) and a short mean time to repair (MTTR). System availability is formally defined as follows:

System Availability =MTTF/(MTTF +MTTR)

System Availability vs. Configuration Size :

Network Threats and Data Integrity

Operational Layers of Distributed Computing System

Four Reference Books:

1. K. Hwang, G. Fox, and J. Dongarra, Distributed and Cloud Computing: from Parallel Processing to the Internet of Things Morgan Kauffmann Publishers, 2011
2. R. Buyya, J. Broberg, and A. Goscinski (eds), Cloud Computing: Principles and Paradigms, ISBN-13: 978-0470887998, Wiley Press, USA, February 2011.
3. T. Chou, Introduction to Cloud Computing: Business and Technology, Lecture Notes at Stanford University and at Tsinghua University, Active Book Press, 2010.
4. T. Hey, Tansley and Tolle (Editors), The Fourth Paradigm : Data-Intensive Scientific Discovery, Microsoft Research, 2009.

Copyright © 2012, Elsevier Inc. All rights reserved.

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