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Grid Computing: A Vertical Market Perspective 2006-2011
a market research report
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Grid computing has moved out of the laboratory and into a wide variety of commercial applications. No longer the exclusive tool of researchers seeking to harness enough compute power for massive computational challenges such as weather modeling or weapons test simulations, today grids are being deployed in more traditional commercial computing applications.
In Grid Computing: A Vertical Market Perspective 2006-2011, Insight Research explores the implications of grid computing on vertical markets and industries, with a special emphasis on the telecommunications industry. Grid computing provides consistent, inexpensive access to computational resources (supercomputers, storage systems, data sources, instruments, and people) regardless of their physical location or access point. As such, The Grid provides a single, unified resource for solving large-scale compute and data intensive computing applications.
In Grid Computing: A Vertical Market Perspective, Insight examines grid technology, the players, and its industry-specific applications, offering segmented forecasts through 2011. In addition to an aggregated spending estimate for grid computing, this report forecasts spending in 14 vertical industries and four geographic regions. Revenue is also segmented by the sharing organization, and by the type of resource shared.
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Report Excerpt
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1.1 Commercial Acceptance of Grid Computing
Since our first analysis of the grid computing market published in 2003, INSIGHT Research has tracked the acceptance of the technology as it moved from the research community into mainstream commercial computing. Our analysis, from the perspective of the mid-point of 2006, suggests grid computing has progressed well into the “early adopters” phase of a new technology lifecycle.
At such a juncture in the new technology adoption curve, it’s not unusual to learn of some successes and of some notable failures. In this respect grid computing is no different than any other technological ingénue that reports early adopter conquests but also confesses to difficulties and problems that come from expectations that remain unmet.
In the past year, grid has racked up some notable “early adopter” milestones on the positive side of the ledger, including:
· Several telecommunications firms, including BT and Telefonica, have selected a grid middleware software partner to build service delivery capabilities;
· A number of grid start-up companies have attracted venture capital funding; and
· Many large enterprises now have partial grid implementations or experiments under way.
On the negative side of the ledger, there have also been some disappointments:
· Using grid computing software is still a challenge. The software—when deployed beyond computational grid applications—is still difficult to use, and the dominant standards remain unstable. As a consequence, there are no stable interoperating implementations based upon the proposed standards.
· Although in science and academia there are many large grids crossing many organizational boundaries, most commercial grids remain behind the firewall and are local to a single enterprise location. We see only a few examples of grids across multiple locations within the same company.
Such successes and shortcomings are fairly typical for an “early adaptor” phase of a new technology adoption curve. We are, nonetheless, beginning to see the first attempts to cross the chasm to the “early majority” phase in at least a few segments, including the technical-engineering and pharmaceutical markets, and to a lesser extent in the financial market.
1.2 What is Grid Computing?
Grid computing is a form of distributed system wherein computing resources are shared across networks. Just as Web standards and technologies enabled universal, transparent access to documents, grid promises do so for computing resources. Grid enables the selection, aggregation, and sharing of information resources resident in multiple administrative domains and across geographic areas. These information resources are shared based upon their availability, capability, and cost, as well as the user’s quality of service (QoS) requirements. Grid computing is meant to:
· reduce total cost of ownership (TCO);
· aggregate and improve efficiency of computing, data, and storage resources; and
· enable the creation of virtual organizations for applications and data sharing.
IT analysts are calling grid computing one of the outstanding emerging technologies that will likely form the foundation of a fourth wave in IT, as we illustrate in Figure I-1. This nascent fourth stage of IT encompasses technologies and concepts such as grid computing, computing on demand, utility computing, organic information technology (IT), virtualization, adaptive computing, and ....
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Market Segmentation
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By Industry
Healthcare
Construction
Retail Trade
Wholesale Trade
Education/Social Services
Finance/Insurance/Real Estate
Professional Business Services
Hotel and Lodging
Transportation
Communications
Utilities
Entertainment and Media
Durable Manufacturing
Non-durable Manufacturing
By Region
North America
Europe/Middle East/Africa
Central America/Latin America
Asia/Pacific
By Ware
Hardware
Software
Professional Services
By Sharing Organization
Enterprise Grids
Partner Grids
Service Grids
By Resource Shared
Compute Grids
Data Grids
Instrumentation Grids
Application Grids
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Table of Contents
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Chapter I
EXECUTIVE SUMMARY
1.1 Commercial Acceptance of Grid Computing
1.2 What is Grid Computing?
1.3 Grid Computing Implications for Telecom
1.4 Grid Computing Market Analysis
Chapter II
OVERVIEW
2.1 Introduction to Grid Computing
2.1.1 Grid Computing Drivers
2.1.2 Grid Computing Inhibitors
2.1.3 Grid Computing Segmentation
2.2 Understanding Grids as a Tool for Resource Sharing
2.2.1 Compute Grids
2.2.2 Data Grids
2.2.3 Instrumentation and Sensor Grids
2.2.4 Application Grids
2.3 Understanding Grids as Organizational Tools
2.3.1 Enterprise Grids
2.3.1.1 Cluster Grid
2.3.1.2 Campus Grid
2.3.1.3 Enterprise-wide Grid
2.3.2 Partner Grids
2.3.3 Service Grids
2.3.4 State of the Grid
2.4 Related Computing Concepts
2.4.1 Supercomputers
2.4.2 Peer-to-Peer (P2P) Computing
2.4.3 Service-Oriented Architectures
2.4.4 Utility Computing
2.4.5 Autonomic Computing
2.5 Grid Organizations and Standards
2.5.1 Standardization Organizations
2.5.1.1 Global Grid Forum (GGF)
2.5.1.2 Organization for the Advancement of Structured Information Standards (OASIS)
2.5.1.3 Enterprise Grid Alliance (EGA)
2.5.1.4 Distributed Management Task Force (DMTF)
2.5.2 Standards
2.5.2.1 Open Grid Services Architecture (OGSA)
2.5.2.2 Web Services Resource Framework (WSRF)
2.5.2.3 Other Emerging Standards
2.5.3 Toolkits
2.5.3.1 Globus
2.5.3.2 Unicore
2.5.3.3 gLite
2.5.4 Grid Support Centers
2.5.4.1 The Grid Research Integration Development and Support Center (USA)
2.5.4.2 The Open Middleware Infrastructure Institute (UK and EU)
2.5.4.3 Others
Chapter III
APPLICATIONS
3.1 Introduction
3.2 Government and Academic Applications
3.2.1 Government-Sponsored Public Grid Efforts
3.2.1.1 TeraGrid (USA)
3.2.1.2 Open Science Grid (USA)
3.2.1.3 European Union Grid program (EU)
3.2.1.4 NAREGI Grid (Japan)
3.2.1.5 e-Science Program (UK)
3.2.1.6 Other Public Grid Efforts
3.2.1.7 West Virginia Global Grid Exchange
3.2.2 Physical Sciences Applications
3.2.2.1 Search for Extraterrestrial Intelligence (SETI)
3.2.2.2 Earthquake Engineering Simulation
3.2.2.3 High Energy Particle Physics and Earth Observation Applications
3.2.3 Life Sciences Applications
3.2.3.1 Cancer Diagnosis and Screening
3.2.3.2 High Resolution Neurosciences Imaging
3.3 Commercial Applications
3.3.1 Pharmaceutical, Biomedical, and Biotechnological Applications
3.3.1.1 Pharmaceutical Research
3.3.1.2 Protein Analysis
3.3.2 Engineering and Design Automation Applications
3.3.2.1 Airplane Part Design
3.3.2.2 Computer Chip Design
3.3.2.3 Computer Animation and Video Postproduction
3.3.2.4 Aerial and Satellite Image Distribution
3.3.3 Financial Services Applications
3.3.6.1 Investment Banking Applications
3.3.6.2 Life Insurance Financial Modeling Application
3.3.6.3 Risk Management Applications
3.3.6.4 Wachovia Bank
3.3.4 Human Resources Application
3.3.5 Enterprise Data Center Back-up Solution
3.3.6 Information Services Application
3.4 Consumer Applications
Chapter IV
IMPLICATIONS FOR TELECOM
4.1 Grid Computing Implications for Telecom
4.1.1 IT Operations
4.1.2 Bandwidth and Traffic Patterns
4.1.3 Excess Capacity
4.1.4 Next-Generation Telco Services
4.1.5 Potential Roles for Telcos
4.2 Applications Best Suited for Grid Computing
4.3 Case Studies: Grids and Telecom
4.3.1 TeraGrid Case Study
4.3.2 BT Case Study
4.3.3 France Telecom Case Study
4.3.4 Telefonica Case Study
4.4 Grids That Drive Network Innovation
4.4.1 Lambda and Hybrid networking
4.4.2 OptIPuter Project
4.4.3 Akogrimo: A Mobile Grid
4.5 Global Adoption of Grid Technologies
Chapter V
VENDORS 122
5.1 Introduction
5.2 Major IT Platform Providers
5.2.1 Apple Computer
5.2.2 Dell
5.2.3 Hewlett-Packard (HP)
5.2.3.1 Background
5.2.3.2 Grid Solution Stack
5.2.4 International Business Machines (IBM)
5.2.4.1 Background
5.2.4.2 IBM and Grid Computing
5.2.5 Oracle
5.2.6 Sun Microsystems
5.2.6.1 Background
5.2.6.2 Sun N1
5.2.6.3 Sun N1 Grid Engine
5.2.6.4 Utility Computing Services
5.3 Grid Independent Software Companies
5.3.1 Sybase (Avaki)
5.3.1.1 Background
5.3.1.2 Target Markets
5.3.1.3 Technology and Products
5.3.2 DataSynapse, Inc.
5.3.2.1 Background
5.3.2.2 Target Markets
5.3.2.3 Technology and Products
5.3.3 Platform Computing, Inc.
5.3.3.1 Background
5.3.3.2 Target Markets
5.3.3.3 Technology and Products
5.3.3.4 Services
5.3.3.5 Partnerships
5.3.4 United Devices
5.3.4.1 Background
5.3.4.2 Target Markets
5.3.4.3 Technology and Products
5.3.4.4 Business Model and Partnerships
5.3.5 Univa
5.3.6 Base One International
5.3.7 Appistry
5.3.8 Mesh Technologies
5.3.9 Digipede
5.3.10 Others: ActiveGrid, Hemeris, Fujitsu Siemens
Chapter VI
MARKET FORECAST
6.1 Overview
6.1.1 Methodology
6.1.2 Market Segmentation
6.2 Market Model Assumptions
6.2.1 Aggregated IT and Grid Spending
6.2.2 IT and Grid Spending by Vertical Markets
6.2.3 IT and Grid Spending by Region
6.2.4 IT and Grid Spending by Component
6.2.5 Grid Spending by Organization
6.2.6 Grid Spending by Resource
6.3 Forecasts Summary
Appendix
GLOSSARY
TABLE OF FIGURES
Chapter I
I-1 Grid Computing as Part of the IT Evolution
Chapter II
II-1 Grid Computing as Part of the IT Evolution
II-2 Grand Synthesis
II-3 Compute Grid Operation
II-4 Evolution of Grids
II-5 Service Oriented Architecture
II-6 Web Services as an SOA
II-7 Grid Architecture
Chapter III
III-1 Biotech and Pharmaceutical Companies’ Data Management Challenges
III-2 Ad Hoc Solutions Used to Address Data Management Problems
Chapter IV
IV-1 TeraGrid Backplane Architecture
IV-2 TeraGrid National Architecture
IV-3 TeraGrid Site Architecture
IV-4 GLIF Architecture
Chapter V
V-1 The HP Grid Software Stack for the Adaptive Enterprise
TABLE OF TABLES
Chapter I
I-1 Grid Market Segmentation by Resource
I-2 Grid Market Segmentation by Organization
I-3 Worldwide Grid Spending, 2006-2011
Chapter II
II-1 Server and Storage Resource Utilization
II-2 Grid Market Segmentation by Type of Resource
II-3 Grid Market Segmentation by Type of Org
II-4 Supercomputer Distinctions
II-5 Utility Pricing Plans
II-6 Autonomic Computing Attributes
II-7 Web Services Resource Framework Specifications
Chapter III
III-1 Commercial Applications of Grid Computing
III-2 Phased Introduction of Grid Applications
Chapter V
V-1 Grid Vendor Landscape
Chapter VI
VI-1 Grid Market Segmentation by Geography
VI-2 Grid Market Segmentation by Component
VI-3 Grid Market Segmentation by Type of Resource
VI-4 Grid Market Segmentation by Type of Organization
VI-5 Worldwide IT Spending, 2006-2011
VI-6 Worldwide Grid Spending, 2006-2011
VI-7 Worldwide IT Spending by Vertical, 2006-2011
VI-8 Worldwide Grid Computing by Vertical Market, 2006-2011
VI-9 Worldwide IT Spending by Region, 2006-2011
VI-10 Worldwide Grid Spending by Region, 2006-2011
VI-11 Worldwide IT Spending by Component, 2006-2011
VI-12 Worldwide Grid Spending by Component, 2006-2011
VI-13 Worldwide Grid Spending by Type of Organization, 2006-2011
VI-14 Worldwide Grid Spending by Type of Resource, 2006-2011
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