1. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING UNIT-1 TECHNOLOGIES FOR NETWORK-BASED SYSTEMS Dr.K.Venkata Subba Reddy, Professor and HOD, Department of CSE, KHIT, Guntur KALLAM HARANADHAREDDY INSTITUTE OF TECHNOLOGY, GUNTUR 1/112

2. 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

3. 3/112 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

4. 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 4/112

5. 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 5/112

6. MULTICORE CPU Vs MULTITHREADING  Multicore refers to a computer or processor that has more than one logical CPU core, and that can physically execute multiple instructions at the same time.  Multithreading refers to a program that can running more than one core at the same time. 6/112

7. MULTITHREADING TECHNOLOGY 7/112 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 Multicore refers to a computer or processor that has more than one logical CPU core, and that can physically execute multiple instructions at the same time.

8. 2.2 GPU COMPUTING TO EXASCALE AND BEYOND 8/112 Graphics Processing Unit-GPU was marketed by NVIDIA in 1999 It is a graphics coprocessor or accelerator mounted on a computer’s graphics card or video card. 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. For example, the Xeon X5670 CPU has six cores. However, a modern GPU chip can be built with hundreds of processing cores.

9. GPU COMPUTING 9/112 A CPU consists of four to eight CPU cores, while the GPU consists of hundreds of smaller cores. The NVIDIA, CUDA, Tesla is used in GPU clusters or in High Processing Systems(HPS) for parallel processing of massive floating-pointing data.

10. Four challenges are identified for exascale computing: (1) Energy and power, (2) Memory and storage, (3) Concurrency and locality (4) System resiliency (system elasticity) 10/112 GPU Programming Model

11. 11/112 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. The GPU has a many-core architecture that has hundreds of simple processing cores organized as multiprocessors. Each core can have one or more threads. USE OF GPU WITH CPU

12. 2.3.MEMORY STORAGE AND WIDE AREA NETWORKS 12/112 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.

13. 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 13/112 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

14. 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 14/112 HOSTED VIRTUAL MACHINENATIVE VIRTUAL MACHINE

15. 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 15/112

16. BENEFITS OF DATA CENTER VIRTUALIZATION Key benefits of Data Center Virtualization  Reduced Hardware Vendor Lock-in  Improved Disaster Recovery  Smooth Migration to Cloud  Reduced Data Center Footprint  Faster Server Provisioning. 16/112

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