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