Lecture notes on High Performance computing

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LECTURE NOTES ON HIGH PERFORMANCE COMPUTING LECTURE NOTES on HIGH PERFORMANCE COMPUTING Course Code: BCS 425 DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTES ON HIGH PERFORMANCE COMPUTING SYLLABUS Module – I Cluster Computing: Introduction to Cluster Computing, Scalable Parallel Computer Architectures, Cluster Computer and its Architecture, Classifications, Components for Clusters, Cluster Middleware and Single System Image, Resource Management and Scheduling, Programming Environments and Tools, Applications, Representative Cluster Systems, Heterogeneous Clusters, Security, Resource Sharing, Locality, Dependability, Cluster Architectures, Detecting and Masking Faults, Recovering from Faults, Condor, Evolution of Metacomputing. Module – II Load Sharing and Balancing: Evolution, Job and Resource Management Systems, State-of-the- Art in RMS and Job, Rigid Jobs with Process Migration, Communication-Based Scheduling, Batch Scheduling, Fault Tolerance, Scheduling Problem for Network Computing, Algorithm - ISH, MCP and ETF, Dynamic Load Balancing, Mapping and Scheduling, Task Granularity and Partitioning, Static and Dynamic Scheduling. Module - III Grid Computing: Introduction to Grid Computing, Virtual Organizations, Architecture, Applications, Computational, Data, Desktop and Enterprise Grids, Data-intensive Applications, High-Performance Commodity Computing, High-Performance Schedulers, Grid Middleware: Connectivity, Resource and Collective Layer, Globus Toolkit, GSI, GRAM, LDAP, GridFTP, GIIS, Heterogeneous Computing Systems, Mapping Heuristics: Immediate and Batch Mode, Immediate: MCT, MET, Switching Algorithm, KPB and OLB, Batch: Min-Min, Max-Min, Sufferage, Duplex, GA, SA, GSA, Tabu and A, Expected Time to Compute Matrix, Makespan, Heterogeneity: Consistent, Inconsistent and Partially-Consistent, QoS Guided Min-Min, Selective Algorithm, Grid Computing Security, Introduction to GridSim, Architecture, Grid Resource Broker, Grid Referral Service. Module - IV Cloud Computing: Introduction to Cloud Computing, Types: Deployment and Service Models, Characteristics, Applications, Service-Level Agreement, Virtualization, High-Throughput Computing: Task Computing and Task-based Application Models, Market-Based Management of Clouds, Energy-Efficient and Green Cloud Computing Architecture, Resource Allocation, Leases, Task Scheduling: RR, CLS and CMMS, Workflow Scheduling, Montage, Epigenomics, SIPHT, LIGO, CyberShake, Task Consolidation, Introduction to CloudSim, Cloudlet, Virtual Machine and its Provisioning, Time and Space-shared Provisioning. DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTES ON HIGH PERFORMANCE COMPUTING Text Books 1. R. Buyya, High Performance Cluster Computing: Architectures and Systems, Volume 1, Pearson Education, 2008. 2. (Edited By) I. Foster and C. Kesselman, The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann, Elsevier, 2004. 3. D. Janakiram, Grid Computing, Tata McGraw-Hill, 2005. 4. R. Buyya, C. Vecchiola and S. T. Selvi, Mastering Cloud Computing Foundations and Applications Programming, Morgan Kaufmann, Elsevier, 2013. Reference Books 1. A. Chakrabarti, Grid Computing Security, Springer, 2007. 2. B. Wilkinson, Grid Computing: Techniques and Applications, CRC Press, 2009. 3. C. S. R. Prabhu, Grid and Cluster Computing, PHI, 2008. 4. B. Sosinsky, Cloud Computing Bible, Wiley, 2011. DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTES ON HIGH PERFORMANCE COMPUTING CONTENTS Module – I: Cluster Computing Lecture 1 Introduction to Cluster Computing Lecture 2 Scalable Parallel Computer Architectures Lecture 3 Cluster Computer and its Architecture, Classifications Lecture 4 Components for Clusters Lecture 5 Cluster Middleware and Single System Image Lecture 6 Resource Management and Scheduling Lecture 7 Programming Environments and Tools, Applications Lecture 8 Representative Cluster Systems, Heterogeneous Clusters Lecture 9 Security, Resource Sharing, Locality, Dependability Lecture 10 Cluster Architectures Lecture 11 Detecting and Masking Faults, Recovering from Faults Lecture 12 Condor, Evolution of Metacomputing Module – II: Load Sharing and Balancing Lecture 13 Evolution, Job and Resource Management Systems Lecture 14 State-of-the-Art in RMS and Job, Rigid Jobs with Process Migration Lecture 15 Communication-Based Scheduling, Batch Scheduling, Fault Tolerance Lecture 16 Scheduling Problem for Network Computing Lecture 17 Algorithm - ISH, MCP and ETF Lecture 18 Dynamic Load Balancing Lecture 19 Mapping and Scheduling, Task Granularity and Partitioning Lecture 20 Static and Dynamic Scheduling Module – III: Grid Computing Introduction to Grid Computing, Virtual Organizations, Architecture, Applications, Lecture 21 Computational, Data, Desktop and Enterprise Grids, Data-intensive Applications High-Performance Commodity Computing, High-Performance Schedulers, Lecture 22 Grid Middleware: Connectivity, Resource and Collective Layer, Globus Toolkit Lecture 23 GSI, GRAM, LDAP, GridFTP, GIIS, Heterogeneous Computing Systems Lecture 24 Mapping Heuristics: Immediate and Batch Mode Lecture 25 Immediate: MCT, MET, Switching Algorithm, KPB and OLB Lecture 26 Batch: Min-Min, Max-Min, Sufferage, Duplex, GA, SA, GSA, Tabu and A Expected Time to Compute Matrix, Makespan, Lecture 27 Heterogeneity: Consistent, Inconsistent and Partially-Consistent Lecture 28 QoS Guided Min-Min, Selective Algorithm Lecture 29 Grid Computing Security Lecture 30 Introduction to GridSim, Architecture, Grid Resource Broker, Grid Referral Service Module –IV: Cloud Computing Introduction to Cloud Computing, Types: Deployment and Service Models, Characteristics, Lecture 31 Applications Lecture 32 Service-Level Agreement, Virtualization Lecture 33 High-Throughput Computing: Task Computing and Task-based Application Models Lecture 34 Market-Based Management of Clouds Lecture 35 Energy-Efficient and Green Cloud Computing Architecture Lecture 36 Resource Allocation, Leases Lecture 37 Task Scheduling: RR, CLS and CMMS Lecture 38 Workflow Scheduling, Montage, Epigenomics, SIPHT, LIGO, CyberShake DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTES ON HIGH PERFORMANCE COMPUTING Lecture 39 Task Consolidation Introduction to CloudSim, Cloudlet, Virtual Machine and its Provisioning, Time and Space- Lecture 40 shared Provisioning DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA DISCLAIMER DISCLAIMER This document does not claim any novelty, originality and it cannot be used as a substitute for prescribed textbooks. The information presented here is merely a collection of knowledge base including research papers, books, online sources etc. by the committee members for their respective teaching assignments. Various online/offline sources as mentioned at the end of the document as well as freely available material from internet were helpful for preparing this document. Citation is needed for some parts of this document. The ownership of the information lies with the respective authors/institution/publisher. Further, this study material is not intended to be used for commercial purpose and the committee members make no representations or warranties with respect to the accuracy or completeness of the information contents of this document and specially disclaim any implied warranties of merchantability or fitness for a particular purpose. The committee members shall not be liable for any loss or profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 1 MODULE - I Introduction to Cluster Computing 1 The essence of Pfister’s 2 and Buyya’s 3 work defines clusters as follows: A cluster is a type of parallel and distributed system, which consists of a collection of inter- connected stand-alone computers working together as a single integrated computing resource. Buyya defined one of the popular definitions for Grids at the 2002 Grid Planet conference, San Jose, USA as follows: A Grid is a type of parallel and distributed system that enables the sharing, selection, and aggregation of geographically distributed ‘autonomous’ resources dynamically at runtime depending on their availability, capability, performance, cost, and users’ quality-of-service requirements. Buyya 1 propose the clouds definition as follows: A Cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resource(s) based on service-level agreements established through negotiation between the service provider and consumers. The computing power of a sequential computer is not enough to carry out scientific and engineering applications. In order to meet the computing power, we can improve the speed of processors, memory and other components of the sequential computer. However, computing power is limited when we consider very complex applications. A cost-effective solution is to connect multiple sequential computers together and combine their computing power. We call it parallel computers. There are three ways to improve performance as per Pfister 1. Non-Technical Term Technical Term 1 Work Harder Faster Hardware Work Smarter More Efficiently 2 3 Get Help Multiple Computer to Solve a Task The evolution of various computing or systems is as follows. Year Computing 1950 Multi-Processors Systems 1960-80 Supercomputers 1988 Reconfigurable Computing 1990 Cluster Computers 1998 Distributed Computing 2000 Grid Computing 2006 SOA and Web Services, Deep Computing, Multi-Core Architecture, Skeleton Based DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 1 MODULE - I Programming, Network Devices 2008 Cloud Computing Heterogeneous Multicore General-Purpose computing on Graphics Processing Units 2009-15 (GPGPU), APU, Big Data There are two eras of computing as follows 3. 1. Sequential Computing Era 2. Parallel Computing Era The computing era is started with improvement of following things 3. 1. Hardware Architecture 2. System Software 3. Applications 4. Problem Solving Environments (PSEs) The components of computing eras are going through the following phases 3. 1. Research and Development 2. Commercialization 3. Commodity A cluster connects a number of computing nodes or personal computers that are used as servers via a fast local area network. It may be a two-node system that connects two personal computers or fast supercomputer. However, the supercomputers may include many clusters. According to the latest TOP500 list 4 (i.e., November 2014), the best 10 supercomputers are as follows. R RMAX RPEAK A POWER SITE SYSTEM CORES (TFLOP (TFLOP N (KW) /S) /S) K Tianhe-2 (MilkyWay-2)- TH- National Super IVB-FEP Cluster, Intel Xeon E5- Computer Center in 1 2692 12C 2.200GHz, TH 3,120,000 33,862.7 54,902.4 17,808 Guangzhou Express-2, Intel Xeon Phi 31S1P China NUDT Titan - Cray XK7 , Opteron 6274 DOE/SC/Oak Ridge 16C 2.200GHz, Cray Gemini 2 National Laboratory 560,640 17,590.0 27,112.5 8,209 interconnect, NVIDIA K20x United States Cray Inc. Sequoia - BlueGene/Q, Power DOE/NNSA/LLNL BQC 16C 1.60 GHz, Custom 1,572,864 17,173.2 20,132.7 7,890 3 United States IBM RIKEN Advanced K computer, SPARC64 VIIIfx Institute for 2.0GHz, Tofu interconnect 705,024 10,510.0 11,280.4 12,660 4 Computational Fujitsu DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 1 MODULE - I Science (AICS) Japan DOE/SC/Argonne Mira - BlueGene/Q, Power BQC 5 National Laboratory 16C 1.60GHz, Custom 786,432 8,586.6 10,066.3 3,945 United States IBM Swiss National Piz Daint - Cray XC30, Xeon E5- Supercomputing 2670 8C 2.600GHz, Aries 6 115,984 6,271.0 7,788.9 2,325 Centre (CSCS) interconnect , NVIDIA K20x Switzerland Cray Inc. Texas Advanced Stampede - PowerEdge C8220, Computing Xeon E5-2680 8C 2.700GHz, 7 Center/Univ. of Infiniband FDR, Intel Xeon Phi 462,462 5,168.1 8,520.1 4,510 Texas SE10P United States Dell JUQUEEN - BlueGene/Q, Power Forschungszentrum BQC 16C 1.600GHz, Custom 8 Juelich (FZJ) 458,752 5,008.9 5,872.0 2,301 Interconnect Germany IBM Vulcan - BlueGene/Q, Power DOE/NNSA/LLNL BQC 16C 1.600GHz, Custom 9 393,216 4,293.3 5,033.2 1,972 United States Interconnect IBM Cray CS-Storm, Intel Xeon E5- Government 2660v2 10C 2.2GHz, Infiniband 72,800 3,577.0 6,131.8 1,499 10 United States FDR, Nvidia K40 Cray Inc. DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 2 MODULE - I Scalable Parallel Computer Architectures 3, 5 The scalable parallel computer architectures are as follows. 1. Massively Parallel Processors (MPP)  It is a shared-nothing architecture.  Each node in MPP runs a copy of the operating systems (OSs). 2. Symmetric Multiprocessors (SMP)  It is a shared-everything architecture. 3. Cache-Coherent Nonuniform Memory Access (CC-NUMA) 4. Distributed Systems  Each node runs its own OS.  It is the combinations of MPPs, SMPs, clusters and individual computers. 5. Clusters The detailed comparisons of the scalable parallel computer architectures are shown below 3, 5. SMP Distributed Characteristic MPP Cluster CC-NUMA 10 to 1000 Number of Nodes 100 to 1000 10 to 100 100 or less Medium or Coarse Wide Range Node Complexity Fine Grain or Medium Medium Grain Grain Message Passing or Shared Files, RPC, Centralized and Internode Shared Variables for Message Passing Distributed Shared Message Passing Communication Distributed Shared and IPC Memory (DSM) Memory Independent Single Run Queue on Multiple Queue but Job Scheduling Single Run Queue Queues Host Coordinated Always in SMP No SSI Support Partially Desired and some NUMA N Micro-Kernels One Monolithic N OS Platform- N OS Platforms Node OS Copies Monolithic or Layered SMP and Many for Homogeneous or Homogeneous and Type Oss NUMA Micro-Kernel Multiple – Single for Multiple Address Space Single Multiple or Single DSM Required if Internode Required Unnecessary Unnecessary Security Exposed Many One or More Ownership One Organization One Organization Organizations Organizations Granularity 6 refers to the extent to which a system or material or a large entity is decomposed into small pieces. Alternatively, it is to the extent for which smaller entities are joined to form a larger entity. It is of two types, namely Coarse-Grained and Fine-Grained. DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 2 MODULE - I A Coarse-Grained defines a system regards large subcomponents of which the larger ones are composed. A Fine-Grained defines a system regards smaller components of which the larger ones are composed. The granularity of data refers to the size in which data fields are sub-divided. For example, the postal address of our Department can be recorded with Coarse-Granularity in a single field as follows. Address = Department of CSE, IT and MCA, VSSUT, Burla, 768018, Odisha, India The same address can be recorded with Fine-Granularity as multiple fields. Department Address = CSE, IT and MCA University = VSSUT City = Burla Postal Code = 768018 State = Odisha Country = India Note that, Fine-Granularity becomes an overhead for data storage. Message Passing 7  Variables have to be marshaled  Cost of communication is obvious  Processes are protected by having private address space  Processes should execute at the same time DSM 7  Variables are shared directly  Cost of communication is invisible  Processes could cause error by altering data  Executing the processes may happen with non-overlapping lifetimes Kernel 8 is a program that manages I/O requests from software and translates them into data processing instructions for the CPU and other electronic components of a computer. A Monolithic Kernel 8 executes all the OS instructions in the same address space in order to improve the performance. A Micro-Kernel 8 runs most of the OS’s background processes in user space to make the OS more modular. Therefore, it is easier to maintain. DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 3 MODULE - I Cluster Computer and its Architecture 3 A Cluster consists of a collection of interconnected stand-alone computers working together as a single computing resource. A computer node can be a single or multi-processor system such as PCs, workstations, servers, SMPs with memory, I/O and an OS. The nodes are interconnected via a LAN. The cluster components are as follows. 1. Multiple High Performance Computers 2. Oss (Layered or Micro-Kernel Based) 3. High Performance Networks or Switches (Gigabit Ethernet and Myrinet) 4. Network Interface Cards (NICs) 5. Fast Communication Protocols and Services (Active and Fast Messages) 6. Cluster Middleware (Single System Image (SSI) and System Availability Infrastructure) 7. Parallel Programming Environments and Tools (Parallel Virtual Machine (PVM), Message Passing Interface (MPI)) 8. Applications (Sequential, Parallel or Distributed) Cluster Classifications 3 The various features of clusters are as follows. 1. High Performance 2. Expandability and Scalability 3. High Throughput 4. High Availability Cluster can be classified into many categories as follows. 1. Application Target  High Performance Clusters  High Availability Clusters 2. Node Ownership  Dedicated Clusters  Nondedicated Clusters 3. Node Hardware  Cluster of PCs (CoPs) or Piles of PCs (PoPs)  Cluster of Workstations (COWs)  Cluster of SMPs (CLUMPs) 4. Node OS  Linux Clusters (Beowulf)  Solaris Clusters (Berkeley NOW)  NT Clusters (High Performance Virtual Machine (HPVM)) DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 3 MODULE - I  Advanced Interactive eXecutive (AIX) Clusters (IBM Service Pack 2 (SP2))  Digital Virtual Memory System (VMS) Clusters  HP-UX Clusters  Microsoft Wolfpack Clusters 5. Node Configuration  Homogeneous Clusters  Heterogeneous Clusters 6. Levels of Clustering  Group Clusters (No. of Nodes = 2 to 99)  Departmental Clusters (No. of Nodes = 10 to 100s)  Organizational Clusters (No. of Nodes = Many 100s)  National Metacomputers (No. of Nodes = Many Departmental or Organizational Systems or Clusters)  International Metacomputers (No. of Nodes = 1000s to Many Millions) DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 4 MODULE - I Components for Clusters 3 The components of clusters are the hardware and software used to build clusters and nodes. They are as follows. 1. Processors  Microprocessor Architecture (RISC, CISC, VLIW and Vector)  Intel x86 Processor (Pentium Pro and II)  Pentium Pro shows a very strong integer performance in contrast to Sun’s UltraSPARC for high performance range at the same clock speed. However, the floating-point performance is much lower.  The Pentium II Xeon uses a memory bus of 100 MHz. It is available with a choice of 512 KB to 2 MB of L2 cache.  Other processors: x86 variants (AMD x86, Cyrix x86), Digital Alpha, IBM PowerPC, Sun SPARC, SGI MIPS and HP PA.  Berkeley NOW uses Sun’s SPARC processors in their cluster nodes. 2. Memory and Cache  The memory present inside a PC was 640 KBs. Today, a PC is delivered with 32 or 64 MBs installed in slots with each slot holding a Standard Industry Memory Module (SIMM). The capacity of a PC is now many hundreds of MBs.  Cache is used to keep recently used blocks of memory for very fast access. The size of cache is usually in the range of 8KBs to 2MBs. 3. Disk and I/O  The I/O performance is improved to carry out I/O operations in parallel. It is supported by parallel file systems based on hardware or software Redundancy Array of Inexpensive Disk (RAID).  Hardware RAID is more expensive than Software RAID. 4. System Bus  Bus is the collection of wires which carries data from one component to another. The components are CPU, Main Memory and others.  Bus is of following types. o Address Bus o Data Bus o Control Bus  Address bus is the collection of wires which transfer the addresses of Memory or I/O devices. For instance, Intel 8085 Microprocessor has an address bus of 16 bits. It shows that the Microprocessor can transfer maximum 16 bit address.  Data bus is the collection of wires which is used to transfer data within the Microprocessor and Memory or I/O devices. Intel 8085 has a data bus of 8 bits. That’s why Intel 8085 is called 8 bit Microprocessor. DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 4 MODULE - I  Control bus is responsible for issuing the control signals such as read, write or opcode fetch to perform some operations with the selected memory location.  Every bus has a clock speed. The initial PC bus has a clock speed of 5 MHz and it is 8 bits wide.  In PCs, the ISA bus is replaced by faster buses such as PCI.  The ISA bus is extended to be 16 bits wide and an enhanced clock speed of 13 MHz. However, it is not sufficient to meet the demands of the latest CPUs, disk and other components.  The VESA local bus is a 32 bit bus that has been outdated by the Intel PCI bus.  PCI bus allows 133 Mbytes/s. 5. Cluster Interconnects  The nodes in a cluster are interconnected via standard Ethernet and these nodes are communicated using a standard networking protocol such as TCP/IP or a low-level protocol such as Active Messages.  Ethernet: 10 Mbps  Fast Ethernet: 100 Mbps  Gigabit Ethernet  The two main characteristics of Gigabit Ethernet are as follows. o It preserves Ethernet’s simplicity which enabling a smooth migration to Gigabit-per-second (Gbps) speeds. o It delivers a very high bandwidth to aggregate multiple Fast Ethernet segments.  Asynchronous Transfer Mode (ATM) o It is a switched virtual-circuit technology. o It is intended to be used for both LAN and WAN, presenting a unified approach to both. o It is based on small fixed-size data packets termed cell. It is designed to allow cells to be transferred using a number of medias such as copper wire and fiber optic cables. o CAT-5 is used with ATM allowing upgrades of existing networks without replacing cabling.  Scalable Coherent Interface (SCI) o It aims to provide a low-latency distributed shared memory across a cluster. o It is design to support distributed multiprocessing with high bandwidth and low latency. o It is a point-to-point architecture with directory-based cache coherence. o Dolphin has produced an SCI MPI which offers less than 12 µs zero message- length latency on the Sun SPARC platform. o DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 4 MODULE - I  Myrinet o It is a 1.28 Gbps full duplex interconnection network supplied by Myricom 9. o It uses low latency cut-through routing switches, which is able to offer fault tolerance. o It supports both Linux and NT. o It is relatively expensive when compared to Fast Ethernet, but has following advantages. 1) Very low latency (5 µs), 2) Very high throughput, 3) Greater flexibility. o The main disadvantage of Myrinet is its price. The cost of Myrinet-LAN components including the cables and switches is 1,500 per host. Switches with more than 16 ports are unavailable. Therefore, scaling is complicated. DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 5 MODULE - I Cluster Middleware and Single System Image 3 Single System Image (SSI) is the collection of interconnected nodes that appear as a unified resource. It creates an illusion of resources such as hardware or software that presents a single powerful resource. It is supported by a middleware layer that resides between the OS and the user-level environment. The middleware consists of two sub-layers, namely SSI Infrastructure and System Availability Infrastructure (SAI). SAI enables cluster services such as checkpointing, automatic failover, recovery from failure and fault-tolerant. 1. SSI Levels or Layers  Hardware (Digital (DEC) Memory Channel, Hardware DSM and SMP Techniques)  Operating System Kernel – Gluing Layer (Solaris MC and GLUnix)  Applications and Subsystems – Middleware o Applications o Runtime Systems o Resource Management and Scheduling Software (LSF and CODINE) 2. SSI Boundaries  Every SSI has a boundary.  SSI can exist at different levels within a system – one able to be built on another 3. SSI Benefits  It provides a view of all system resources and activities from any node of the cluster.  It frees the end user to know where the application will run.  It frees the operator to know where a resource is located.  It allows the administrator to manage the entire cluster as a single entity.  It allows both centralize or decentralize system management and control to avoid the need of skilled administrators for system administration.  It simplifies system management.  It provides location-independent message communication.  It tracks the locations of all resources so that there is no longer any need for system operators to be concerned with their physical location while carrying out system management tasks. 4. Middleware Design Goals  Transparency  Scalable Performance  Enhanced Availability 5. Key Service of SSI and Availability Infrastructure  SSI Support Services o Single Point of Entry o Single File Hierarchy o Single Point of Management and Control DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 5 MODULE - I o Single Virtual Networking o Single Memory Space o Single Job Management System o Single User Interface  Availability Support Functions o Single I/O Space o Single Process Space o Checkpointing and Process Migration DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 6 MODULE - I Resource Management and Scheduling (RMS) 3 RMS is the process of distributing user’s applications among computers to maximize the throughput. The software that performs the RMS has two components, namely resource manager and resource scheduler. Resource manager deals with locating and allocating computational resources, authentication, process creation and migration whereas the resource scheduler deals with queuing applications, resource location and assignment. RMS is a client-server system. The jobs are submitted to the RMS environment and the environment is responsible for place, schedule and run the job in the appropriate way. The services provided by a RMS environment are as follows. 1. Process Migration 2. Checkpointing  Taxonomy of Checkpoint Implementation 14 o Application-Level  Single Threaded  Multi Threaded  Mig Threaded o User-Level  Patch  Library o System-Level  Kernel-level  Hardware-level 3. Scavenging Idle Cycles 4. Fault Tolerance 5. Minimization of Impact on Users 6. Load Balancing 7. Multiple Application Queue There are many commercial and research packages available for RMS as follows. 1. LSF (http://www.platform.com/)  The full form of LSF is Load Sharing Facility  Fair Share  Preemptive  Backfill and Service Level Agreement (SLA) Scheduling  High Throughput Scheduling  Multi-cluster Scheduling  Topology, Resource and Energy-aware Scheduling DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA LECTURE NOTE – 6 MODULE - I  LSF is a job scheduling and monitoring software system developed and maintained by Platform Computing.  LSF is used to run jobs on the blade center.  A job is submitted from one of the head nodes (login01, login02 for 32-bit jobs, login03 for jobs compiled to use 64-bits) and waits until resources become available on the computational nodes.  Jobs which ask for 4 or fewer processors and 15 minutes or less time are given a high priority. 2. CODINE (http://www.genias.de/products/codine/tech_desc.html)  The full form of CODINE is Computing in Distributed Networked Environments.  Advanced Reservation  CODINE was a grid computing computer cluster software system, developed and supported by Sun Microsystems and later Oracle 12. Figure 1 CODINE 13 DEPARTMENT OF CSE & IT, VSSUT, BURLA – 768018, ODISHA, INDIA

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