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IBM Technical Computing Clouds

IBM Technical Computing Clouds

by Stefan Velica, Shao Feng Shi, Kailash Marthi, Hua Chen Li, Victor Hu, Amitava Ghosal, Rodrigo Garcia da Silva, Murali Dhandapani, Rodrigo Ceron, Dino Quintero
Publisher: IBM Redbooks
Release Date: October 2013
ISBN: 0738438782
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Book Description

This IBM® Redbooks® publication highlights IBM Technical Computing as a flexible infrastructure for clients looking to reduce capital and operational expenditures, optimize energy usage, or re-use the infrastructure.

This book strengthens IBM SmartCloud® solutions, in particular IBM Technical Computing clouds, with a well-defined and documented deployment model within an IBM System x® or an IBM Flex System™. This provides clients with a cost-effective, highly scalable, robust solution with a planned foundation for scaling, capacity, resilience, optimization, automation, and monitoring.

This book is targeted toward technical professionals (consultants, technical support staff, IT Architects, and IT Specialists) responsible for providing cloud-computing solutions and support.

Table of Contents

  1. Front cover
  2. Notices
    1. Trademarks
  3. Preface
    1. Authors
    2. Now you can become a published author, too!
    3. Comments welcome
    4. Stay connected to IBM Redbooks
  4. Chapter 1. Introduction to technical cloud computing
    1. 1.1 What is Technical Computing
      1. 1.1.1 History
      2. 1.1.2 Infrastructure
      3. 1.1.3 Workloads
    2. 1.2 Why use clouds?
      1. 1.2.1 Flexible infrastructure
      2. 1.2.2 Automation
      3. 1.2.3 Monitoring
    3. 1.3 Types of clouds
  5. Chapter 2. IBM Platform Load Sharing Facilities for technical cloud computing
    1. 2.1 Overview
    2. 2.2 IBM Platform LSF family features and benefits
      1. 2.2.1 IBM Platform Application Center (PAC)
      2. 2.2.2 IBM Platform Process Manager (PPM)
      3. 2.2.3 IBM Platform License Scheduler
      4. 2.2.4 IBM Platform Session Scheduler
      5. 2.2.5 IBM Platform Dynamic Cluster
      6. 2.2.6 IBM Platform RTM
      7. 2.2.7 IBM Platform Analytics
    3. 2.3 IBM Platform LSF job management
      1. 2.3.1 Job submission
      2. 2.3.2 Job status
      3. 2.3.3 Job control
      4. 2.3.4 Job display
      5. 2.3.5 Job lifecycle
    4. 2.4 Resource management
    5. 2.5 MultiCluster
      1. 2.5.1 Architecture and flow
      2. 2.5.2 MultiCluster models
  6. Chapter 3. IBM Platform Symphony for technical cloud computing
    1. 3.1 Overview
    2. 3.2 Supported workload patterns
      1. 3.2.1 Compute intensive applications
      2. 3.2.2 Data intensive applications
    3. 3.3 Workload submission
      1. 3.3.1 Commercial applications that are written to the Platform Symphony APIs
      2. 3.3.2 The symexec facility
      3. 3.3.3 Platform Symphony MapReduce client
      4. 3.3.4 Guaranteed task delivery
      5. 3.3.5 Job scheduling algorithms
      6. 3.3.6 Services (workload execution)
    4. 3.4 Advanced resource sharing
      1. 3.4.1 Lending
      2. 3.4.2 Borrowing
      3. 3.4.3 Resource sharing models
      4. 3.4.4 Heterogeneous environment support
      5. 3.4.5 Multi-tenancy
      6. 3.4.6 Resources explained
    5. 3.5 Dynamic growth and shrinking
      1. 3.5.1 Desktop and server scavenging
      2. 3.5.2 Virtual server harvesting
      3. 3.5.3 On-demand HPC capacity
    6. 3.6 Data management
      1. 3.6.1 Data-aware scheduling
    7. 3.7 Advantages of Platform Symphony
      1. 3.7.1 Advantages of Platform Symphony in Technical Computing Cloud
      2. 3.7.2 Multi-core optimizer
  7. Chapter 4. IBM Platform Symphony MapReduce
    1. 4.1 Overview
      1. 4.1.1 MapReduce technology
      2. 4.1.2 Hadoop architecture
      3. 4.1.3 IBM Platform Symphony MapReduce framework
    2. 4.2 Key advantages for Platform Symphony MapReduce
      1. 4.2.1 Higher performance
      2. 4.2.2 Improved multi-tenant shared resource utilization
      3. 4.2.3 Improved scalability
      4. 4.2.4 Heterogeneous application support
      5. 4.2.5 High availability and resiliency
    3. 4.3 Key benefits
  8. Chapter 5. IBM Platform Cluster Manager - Advanced Edition (PCM-AE) for technical cloud computing
    1. 5.1 Overview
    2. 5.2 Platform Cluster Manager - Advanced Edition capabilities and benefits
    3. 5.3 Architecture and components
      1. 5.3.1 Hardware
      2. 5.3.2 External software components
      3. 5.3.3 Internal software components
    4. 5.4 PCM-AE managed clouds support
    5. 5.5 PCM-AE: a cloud-oriented perspective
      1. 5.5.1 Cluster definition
      2. 5.5.2 Cluster deployment
      3. 5.5.3 Cluster flexing
      4. 5.5.4 Users and accounts
      5. 5.5.5 Cluster metrics
  9. Chapter 6. The IBM General Parallel File System for technical cloud computing
    1. 6.1 Overview
      1. 6.1.1 High capacity
      2. 6.1.2 High performance
      3. 6.1.3 High availability
      4. 6.1.4 Single system image
      5. 6.1.5 Multiple operating system and server architecture support
      6. 6.1.6 Parallel data access
      7. 6.1.7 Clustering of nodes
      8. 6.1.8 Shared disks architecture
    2. 6.2 GPFS layouts for technical computing
      1. 6.2.1 Shared disk
      2. 6.2.2 Network block I/O
      3. 6.2.3 Mixed clusters
      4. 6.2.4 Sharing data between clusters
    3. 6.3 Integration with IBM Platform Computing products
      1. 6.3.1 IBM Platform Cluster Manager - Advanced Edition (PCM-AE)
      2. 6.3.2 IBM Platform Symphony
    4. 6.4 GPFS features for Technical Computing
      1. 6.4.1 Active File Management (AFM)
      2. 6.4.2 File Placement Optimizer (FPO)
  10. Chapter 7. Solution for engineering workloads
    1. 7.1 Solution overview
      1. 7.1.1 Traditional engineering deployments
      2. 7.1.2 Engineering cloud solution
      3. 7.1.3 Key benefits
    2. 7.2 Architecture
      1. 7.2.1 Engineering cloud solution architecture
    3. 7.3 Components
      1. 7.3.1 Cloud service consumer
      2. 7.3.2 Security layer
      3. 7.3.3 Cloud services provider
      4. 7.3.4 Systems management
      5. 7.3.5 Third-party products
      6. 7.3.6 Hardware configuration
    4. 7.4 Use cases
      1. 7.4.1 Local workstation and remote cluster
      2. 7.4.2 Thin client and remote cluster
  11. Chapter 8. Solution for life sciences workloads
    1. 8.1 Overview
      1. 8.1.1 Bioinformatics
      2. 8.1.2 Workloads
      3. 8.1.3 Trends and challenges
      4. 8.1.4 New possibilities
    2. 8.2 Architecture
      1. 8.2.1 Shared service models
      2. 8.2.2 Components
    3. 8.3 Use cases
      1. 8.3.1 Mixed workloads on hybrid clouds
      2. 8.3.2 Integration for life sciences private clouds
      3. 8.3.3 Genome sequencing workflow with Galaxy
  12. Chapter 9. Solution for financial services workloads
    1. 9.1 Overview
      1. 9.1.1 Challenges
      2. 9.1.2 Types of workloads
    2. 9.2 Architecture
      1. 9.2.1 IBM Platform Symphony
      2. 9.2.2 General Parallel File System (GPFS)
      3. 9.2.3 IBM Platform Process Manager (PPM)
    3. 9.3 Use cases
      1. 9.3.1 Counterparty CCR and CVA
      2. 9.3.2 Shared grid for high-performance computing (HPC) risk analytics
      3. 9.3.3 Real-time pricing and risk
      4. 9.3.4 Analytics for faster fraud detection and prevention
    4. 9.4 Third-party integrated solutions
      1. 9.4.1 Algorithmics Algo One
      2. 9.4.2 SAS
  13. Chapter 10. Solution for oil and gas workloads
    1. 10.1 Overview
      1. 10.1.1 Enhance exploration and production
      2. 10.1.2 Workloads
      3. 10.1.3 Application software
    2. 10.2 Architecture
      1. 10.2.1 Components
  14. Chapter 11. Solution for business analytics workloads
    1. 11.1 MapReduce
      1. 11.1.1 IBM InfoSphere BigInsights
      2. 11.1.2 Deploying a BigInsights workload inside a cloud
    2. 11.2 NoSQL
      1. 11.2.1 HBase
  15. Related publications
    1. IBM Redbooks
    2. Other publications
    3. Online resources
    4. Help from IBM
  16. Back cover