Gridcomputingppt

GRID
COMPUTING
NAME:Navpreet Kaur
BRANCH: CSE(Evening Shift)
ROLL NO.: 115361

 What is Grid Computing?
 Cousins of grid computing.
 Methods of grid computing.
 Who Needs It?
 Grid Users
 Some highly visible grids.
 Using the grid.

What is grid computing?
 Grid computing involves connecting
geographically remote computers into a single
network to create a virtual supercomputer by
combining the computational power of all
computers on grid.
 COMPUTATIONAL GRIDS

 Homogeneous
 Heterogeneous

 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

Cousins of Grid Computing
 Distributed Computing
 Peer-to-Peer Computing etc.

 Distributed Computing
Distributed computing is most often
concerned with distributing the load of a
program across two or more processes

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.

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

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.

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.

On-Demand Computing
 Uses grid capabilities to meet short-term
requirements for resources that are not locally
accessible.

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.

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

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.

Who Needs Grid Computing?
 A chemist may utilize hundreds of processors
to screen thousands of compounds per hour.
 Teams of engineers worldwide pool resources
to analyze terabytes of structural data.
 Meteorologists seek to visualize and analyze
data of climate with enormous computational
demands.

Grid Users
 Grid developers
 Tool developers
 Application developers
 End Users
 System Administrators

Grid Developers
 Very small group.
 Implementers of a grid “protocol” who
provides the basic services required to
construct a grid.

Tool Developers
 Implement the programming models used by
application developers.
 Implement basic services similar to
conventional computing services:
 User authentication/authorization
 Process management
 Data access and communication

Application Developers
 Construct grid-enabled applications for end-
users who should be able to use these
applications without concern for the
underlying grid.
 Provide programming models that are
appropriate for grid environments and services
that programmers can rely on when
developing (higher-level) applications.

System Administrators
 Balance local and global concerns.
 Manage grid components and infrastructure.
 Some tasks still not well delineated due to the
high degree of sharing required.

Some Highly-Visible Grids

 The NASA Information Power Grid (IPG).
 The Distributed Terascale Facility (DTF)
Project.

Software infrastructure
 Globus
 Condor
 Harness
 Legion
 IBP
 Net Solve

Globus
 started in 1996 and is gaining popularity year
after year.
 A project to develop the underlying
technologies needed for the construction of
computational grids.
 Focuses on execution environments for
integrating widely-distributed computational
platforms, data resources, displays, special
instruments and so forth.

Condor
 The Condor project started in 1988 at the
University of Wisconsin-Madison.
 The main goal is to develop tools to support
High Throughput Computing on large
collections of computing resources.

Legion
 An object-based software project designed at
the University of Virginia to support millions
of hosts and trillions of objects linked together
with high-speed links.
 Allows groups of users to construct shared
virtual work spaces, to collaborate research
and exchange information.

Harness
 A Heterogeneous Adaptable Reconfigurable
Networked System
 A collaboration between Oak Ridge National
Lab, the University of Tennessee, and Emory
University.

IBP
 The Internet Backplane Protocol (IBP) is a
middleware for managing and using remote
storage.
 It was devised at the University of Tennessee
to support Logistical Networking in large
scale, distributed systems and applications.

NetSolve
 A client-server-agent model.
 Designed for solving complex scientific
problems in a loosely-coupled heterogeneous
environment.

CONCLUSION
 Grid Computing involves cost savings, speed of
computation, and agility.
 The grid adjusts to accommodate the fluctuating data
volumes that are a typical in the seasonal business.
 Grid Computing takes advantage of the fact that most
of the computers in United States use their central
processing units on average only 25% of the time for
the work they have been assigned.

Gridcomputingppt

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Gridcomputingppt