1. Grid Computing

2. 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.

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

5. 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 1 2 4 3

6. 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.

7. Grid Security Model

8. TYPES OF GRID
Computational Grid
Scavenging Grid
Data Grid

9. 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.

10. 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.

11. 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.

12. Cousins of Grid Computing Methods of Grid Computing
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

13. Each user should have a single login account to access all resources.
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.

14. Grids are typically managed by gridware.
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…)

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

16. 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.

17. 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.

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

19. 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.

20. 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.

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

22. 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.

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

24. 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.

25. 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.

26. 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.

27. 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.

28. Thanks….!!!!