Grid computing allows for sharing and distribution of computing power, data, and other resources across dynamic, decentralized networks. It provides transparent access to resources regardless of location, enabling applications to execute on the most appropriate systems. The key advantages are increased user productivity through faster completion of work, and scalability to grow computing power seamlessly over time. Grids have various applications including distributed supercomputing, high-throughput computing, and data-intensive computing.

1. Grid
Computing

2. Content
 Introduction
 What is Grid Computing
 Who can use Grid Computing
 Application
 How Grid Computing Works
 Architecture

3. Content
 Types of Grid
 Advantages
 Disadvantages
 Conclusion

4. What is Grid Computing?
 Grid computing is a type of data management and computer
infrastructure, designed as a support primarily for scientific
research, but, also used in various commercial concepts,
business research, entertainment and finally by governments
of different countries.

5. Who can use grid computing
 Governments and International Organizations
 The military
 Teachers and educators
 Businesses

7. A typical view of Grid environment
User
Resource Broker
Grid Resources
Grid Information Service
A User sends computation or data
intensive application to Global Grids in
order to speed up the execution of the
application.
A Resource Broker distribute the jobs in an
application to the Grid resources based on user’s
QoS requirements and details of available Grid
resources for further executions.
Grid Resources (Cluster, PC,
Supercomputer, database, instruments, etc.) in
the Global Grid execute the user jobs.
Grid Information Service system
collects the details of the available
Grid resources and passes the
information to the resource broker.
Computation result
Grid application
Computational jobs
Details of Grid resources
Processed jobs
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2
3
4

8. Grid architecture

9. Grid architecture
 Fabric layer: Provides the resources to which shared access is mediated by Grid
protocols.
 Connectivity layer: Defines the core communication and authentication
protocols required for grid-specific network functions.
 Resource layer: Defines protocols, APIs, and SDKs for secure negotiations,
initiation, monitoring control, accounting and payment of sharing operations on
individual resources.
 Collective Layer: Contains protocols and services that capture interactions
among a collection of resources.
 Application Layer: These are user applications that operate within VO
environment.

10. TYPES OF GRID
• Computational Grid
• Scavenging Grid
• Data Grid

11. Computational Grid
• A computational grid is focused on setting aside
resources specifically for computing power.
• In this type of grid, most of the machines are
high-performance servers.

12. Scavenging Grid
A scavenging grid is most commonly used with large
numbers of desktop machines.
 Machines are scavenged for available CPU cycles and other
resources.
 Owners of the desktop machines are usually given control
over when their resources are available to participate in the
grid.

13. Data Grid
 A data grid is responsible for housing and providing access
to data across multiple organizations.
 Users are not concerned with where this data is located as
long as they have access to the data.

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What is Grid Computing?
Computational Grids
Homogeneous (e.g., Clusters)
Heterogeneous (e.g., with one-of-a-kind
instruments)
Cousins of Grid Computing
Methods of Grid Computing

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Computational Grids
A network of geographically distributed
resources including computers, peripherals,
switches, instruments, and data.
Each user should have a single login account to
access all resources.
Resources may be owned by diverse
organizations.

16. 16
Computational Grids
Grids are typically managed by gridware.
Gridware can be viewed as a special type of
middleware that enable sharing and manage grid
components based on user requirements and resource
attributes (e.g., capacity, performance, availability…)

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Cousins of Grid Computing
Parallel Computing
Distributed Computing
Peer-to-Peer Computing
Many others: Cluster Computing, Network Computing,
Client/Server Computing, Internet Computing, etc...

18. 18
Distributed Computing
People often ask: Is Grid Computing a fancy new
name for the concept of distributed computing?
In general, the answer is “no.” Distributed Computing
is most often concerned with distributing the load of a
program across two or more processes.

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PEER2PEER Computing
Sharing of computer resources and services by
direct exchange between systems.
Computers can act as clients or servers
depending on what role is most efficient for the
network.

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Methods of Grid Computing
 Distributed Supercomputing
 High-Throughput Computing
 On-Demand Computing
 Data-Intensive Computing
 Collaborative Computing
 Logistical Networking

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Distributed Supercomputing
Combining multiple high-capacity resources on
a computational grid into a single, virtual
distributed supercomputer.
Tackle problems that cannot be solved on a
single system.

22. 22
High-Throughput Computing
Uses the grid to schedule large numbers of
loosely coupled or independent tasks, with the
goal of putting unused processor cycles to work.

23. 23
On-Demand Computing
Uses grid capabilities to meet short-term
requirements for resources that are not locally
accessible.
Models real-time computing demands.

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Data-Intensive Computing
The focus is on synthesizing new information
from data that is maintained in geographically
distributed repositories, digital libraries, and
databases.
Particularly useful for distributed data mining.

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Collaborative Computing
Concerned primarily with enabling and
enhancing human-to-human interactions.
Applications are often structured in terms
of a virtual shared space.

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Logistical Networking
 Global scheduling and optimization of data movement.
 Contrasts with traditional networking, which does not explicitly
model storage resources in the network.
 Called "logistical" because of the analogy it bears with the
systems of warehouses, depots, and distribution channels.

27. Advantages
 Increased user productivity: By providing transparent
access to resources, work can be completed more quickly.
 Scalability: Grids can grow seamlessly over time, allowing
many thousands of processors to be integrated into one cluster.
 Flexibility: Grid computing provides computing power where
it is needed most, helping to better meet dynamically changing
work loads.

28. Disadvantages
1) For memory hungry applications that can't take advantage, you may be
forced to run on a large systems.
2) You may need to have a fast interconnect between compute resources
(gigabit Ethernet at a minimum).
3) Some applications may need to be tweaked to take full advantage of the
new model.
4) Licensing across many servers may make it prohibitive for some apps.
Vendors are starting to be more flexible with environment like this.

29. CONCLUSION
 Grid computing introduces a new concept to IT infrastructures
because it supports distributed computing over a network of
heterogeneous resources and is enabled by open standards.

30. Thanks….!!!!