1. Case Study Report Presentation
Grid computing: The Grid
(Mar 05, 2013)
By
Jivan Nepali, 066 BCT 517
Laxmi Kadariya, 066 BCT 518
Narayan Pd. Kandel, 066 BCT 520

2. IS There Any Computer System
that isn’t a Grid?
Bio Data Knowledge
Grids Grids Grids
Commodity Compute Science
Grids Grids Grids
Cluster Tera Sensor
Grids Grids Grids

3. Definitions
 “A computational grid is a hardware and software
infrastructure that provides dependable,
consistent, pervasive, and inexpensive access to
high-end computational capabilities.”
– Foster & Kesselman, 1998
 “Grid computing is concerned with coordinated
resource sharing and problem solving in dynamic,
multi-institutional virtual organizations (VOs).”
- Foster & Tuecke, 2000
It is NOT a cluster Architecture!

4. Grid: Building Blocks
 Networks
 Link together geographically distributed resources and
allow them to be used collectively
 Computational ‘Nodes’ on the Grid
 Networks connect resources on the Grid –
computational resources with data storage
 Pulling it together
 It involves the coordination and partnerships among
the remaining blocks for the complete model of a Grid
 Common Infrastructure: Standards
 Grid Standards to developers & users
 Standards on which the Grid is being built

5. Grid Computing: Architectural
Model
 Hourglass Model
 Thin center: few standards Application Layer
 Wide top: many high-level
Collective Layer
behaviors can be mapped
 Wide bottom: many underlying
Connectivity Layer Resource Layer
technologies and systems
Fabric Layer

6. Grid Models
 Distributed Super-Computing
 Aggregate computational resources to tackle problems that cannot
be solved by a single system
 High-throughput Computing
 Schedule large numbers of independent tasks to exploit unused
CPU cycles
 On-demand Computing
 Use Grid capabilities to meet short-term requirements for
resources that cannot conveniently be located locally
 Data-Intensive Computing
 Synthesize data in geographically distributed repositories
 Collaborative Computing
 Enable shared use of data archives and simulations

7. Grid Computing: Challenges
 No clear Standard
 Debate on Concept
 Difficult to Develop
 Limited area & Applications
 Lack of Grid-enabled Software
 Centralized Management
 Security
 Management and Administration

8. Grid Computing: Applications
 Life Science Application
 Computational biology, bioinformatics, genomics,
computational neuroscience
 e.g. the Protein Data Bank, the myGrid Project, the
Biomedical Information Research Network (BIRN),
MCell].
 Engineering-oriented Application
 NASA operation its research through grid
 Data-oriented Application
 Data is emerging as the ‘killer application’ of the Grid.
 e.g. Distributed Aircraft Maintenance Environment
(DAME).

9. Grid Computing: Applications
Cont...
 Physical Science Application
 The National Virtual Observatory Project in the United States
is using the Grid to federate sky surveys from several
different telescopes
 Commercial Application
 Virtual server hosting,
 Disaster recovery,
 Heterogeneous workload management,
 End-to-end systems management,
 End-to-end automation
 Reducing up-front investment
 Accessing new capability more quickly,
 Better performance

10. References
[1] Foster, I. & Kesselman C. (2005). “The Grid in a
Nutshell”. Mathematics and Computer Science Division,
Argonne National Laboratory, Information Sciences
Institute, University of Southern California, USA.
[2] Engelen van R. (2008). “Concepts & Architecture of
Grid Computing”. Leiden University, Netherlands.
[3] Berman F., Hey A.J.G. & Fox G.C. (2003). “Grid
Computing – Making the Global Infrastructure a
Reality”. John Wiley & Sons Ltd, Chichester, England.
[4] Abbas A. (2004). “Grid Computing: A Practical Guide
to Technology & Applications”. Firewall Media, An
Imprint of Laxmi Publications Pvt. Ltd., Golden House,
Daryaganj, New Delhi, India.

11. Thank You!