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시장보고서
Grid Computing: A Vertical Market Perspective 2006-2011
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Abstract
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.
Report Excerpt:
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 ingenue 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 ....
Table of Contents
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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