This document provides an overview of grid computing. It discusses that grid computing enables sharing, selection, and aggregation of distributed resources like supercomputers, storage, and data sources. Grid computing allows for these resources to be used as a unified virtual machine. The document then discusses the services offered by grids including computational, data, application, information, and knowledge services. It also discusses the types of grids like computational grids, data grids, and scavenging grids. Finally, it discusses some of the key advantages of grid computing like making better use of available hardware and idle computing resources.

1. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 6, August 2012
Grid Computing
Naveen Kumar*, Naveen Kumar, Rajbir Singh, Vaibhav Arora, Vikas Rohilla

Abstract— "The Grid" means the substructure for the I. INTRODUCTION
Advanced Web, for computing, assistance and
correlation. Grid is a type of collateral and distributed The term Grid computing was born in the early 1990s as a
system that enables the sharing, assortment, and allegory for making computer power to work as easy to
aggregation of geographically varied "autonomous" access as an electric power grid in "The Grid: Blueprint for a
resources dynamically at runtime depending on their new computing infrastructure".
availability, capability, performance, cost, and users'
quality-of-service requirements. In simplest way grid The fame of the Internet as well as the availability of rich and
computing is distributed computing taken to the next powerful computing gears and high-speed network
evolutionary grade. Having an objective to blueprint a methodologies as low-cost commodity components is turning
delusion of simple yet large and dominant self managing the table upside down the way we use computers in today’s
virtual machine (computer) out of a large grid of linked world. These technology opportunities have led a path to the
heterogeneous systems sharing various assets. possibility of usage of distributed computers as a solo,
unified computing resource, leading to Grid computing.
Grid computing service allows grid users to do any sort of
computation that needs any category of hardware or The grid approach to network computing is known by
software resource, with restricted resources at the client various names, such as metacomputing, scalable computing,
side. The projected grid computing service takes into global computing, Internet computing, and more recently
description both hardware and software necessities of the peer to peer (P2P) computing [1].
submitted computing task. On the other hand our grid
system needs to make the most of the overall system Grids enable the sharing, selection, and collectivity of wide
throughput, play down the response time and all good & varied resources including supercomputers, data sources,
resource exploitation. In grid computing we try to clump and specialized devices which are geographically distinct and
wide variety of geographically scattered resources, such owned by separate organizations for solving large-scale data
as supercomputers, storage systems, data sources, and vehement problems in science & engineering. Thus creating
exceptional devices, that can then be used as a unified virtual organizations as envisioned in as a temporary alliance
resource and thus form what is prevalently known as of that come together to share core competencies, or
“Computational Grids”. resources in order to better respond to business opportunities
or large-scale application processing needs, and whose
Index Terms—grid, self-managing, computational grid cooperation is supported by networks.
Naveen Kumar*, 12071, Department of Computer Science Engineering,
Dronacharya College of Engineering, (e-mail: menaveenkumar@live.com).
Gurgaon, India, +91-9958919990
Naveen Kumar, 12070, Department of Computer Science Engineering,
Dronacharya College of Engineering, (e-mail: rananaveen91@gmail.com).
Gurgaon, India, +91-9958919990
Rajbir Singh, Department of Computer Science Engineering, Dronacharya
College of Engineering, (e-mail: rajbirsingh455@gmail.com). Gurgaon,
India, +91-9958919990
Figure.1: Grid Computing
Vaibhav Arora, Department of Computer Science Engineering,
Dronacharya College of Engineering, (e-mail: menaveenkumar@live.com).
Gurgaon, India, +91-9718275095 As in Fig. 1, the grid is a virtual platform for computing and
data management substructure.
Vikas Rohilla, Department of Computer Science Engineering, Dronacharya
College of Engineering, (e-mail: vikasrohilla@live.com). Gurgaon, India,
+91-9990787188 • Useful for society globally
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2. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 6, August 2012
– Business
Knowledge services: These are concerned with the way that
– Government knowledge is used, published, and maintained for the
assistance of its users in achieving their goals. Knowledge is
understood as information applied to achieve a goal, solve a
– Research problem, or execute a pending decision. An example of this is
data mining.
– Science and information
• Dynamically connect together resources III. GRID CONSTRUCTION
• Enables to operate on large scale , resource vehement, Some of the general principles that governs the design
features of the grid are:
and distinct applications
Multiple administrative domains and autonomy: Grid
• One way -> Parallel and distributed ambience resources are geographically dispensed across multiple
domains and ownership partnered by several organizations.
• Apportion, selection, aggregation of geographically The independence of resource owners needs to be honored
distinct independent resources at time relying on their along with their resource management and usage policies.
accessibility, capability, efficiency, cost and final quality
of service requirements. Heterogeneity: A Grid involves a multiplicity of resources
that are heterogeneous in nature and will enclose a good
range of methods & technologies.
II. SERVICES OFFERED BY GRID
Scalability: A Grid might grow from a few integrated
resources to millions. This risks the problem of performance
Computational services: These are concerned with degradation with the increasing size of the Grids.
providing secure & powerful yet efficient services for Subsequently, apps that need a large number of
operating application services on distributed computational geographically located resources must be designed to be
resources individually or collectively. Resource brokers latent and bandwidth tolerant.
provide accessibility to the services for collective use of
distributed resources. A Grid providing these services is Dynamicity or Adaptability: Resource failure is the rule
often called a Computational Grid. Examples of rather than the exception in a grid. With many resources in a
Computational Grids include NASA IPG, the World Wide Grid, the failure of resources is probable. Resource managers
Grid, and the NSF TeraGrid [2]. must execute their behavior dynamically and use the existing
arsenal of resources efficiently and effectively.
Data services: These are concerned with providing access to
distributed datasets securely and their management on a high  Steps to realize a grid:
end basis. To provide a scalable storage and access to the data
sets, they may be duplicated and even different datasets (i) Collection of individual software and hardware
stored in different portions to create an illusion of mass components into a solo unified network resource.
storage. The processing of datasets is carried out using
Computational Grid services. Such a combo is popularly (ii) Unfurl the low level middleware and user level
known as Data Grids. Sample applications that need such middleware to provide secure access to resources.
services for management and processing of huge datasets are
high-energy physics and accessibility to distributed chemical (iii) Optimization of distinct applications to take advantage of
databases for drug design. existing substructure.
Application services: These are mainly concerned with app
 Basic architectural components required to construct a
management and providing accessibility to remote software
grid:
and libraries transparently. The rising technologies such as
Web services are expected to play a lead role in defining
Grid Fabric: All the resources distributed globally that are
application jobs. They build on computational services
accessible from anywhere on the Internet.
provided by the Grid. NetSolve can be used to develop such
services.
Core Grid Middleware: This offers core services such as
remote process management, storage access, information
Information services: These are concerned with the export
registration, and security.
and presentation of data by making use of the services of
User-level Grid middleware: This includes application
computational, data, and application services. Given its key
development milieus, programming tool ware and reserve
role in many scientific arrangements, the Web is the obvious
brokers for managing resources and arranging application
point of departure for this level.
errands for execution on global resources.
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3. ISSN: 2277 – 9043
International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 6, August 2012
Grid applications and portals: Grid applications are
typically developed using Grid-enabled languages such as
HPC++ or MPI. An example is a stricture simulation or a
grand-challenge problem [3].
Figure 3: Scheduler
Data management: If any data -- together with application
modules -- must be moved or made handy to the nodes where
an application's jobs will execute, then there needs to be a
safe and sound and unswerving method for moving files and
data to various nodes contained by the grid.
Job and resource management: With all the other
amenities, we now get to the hub set of services that help
carry out actual work in a grid upbringing [5]. The Grid
Resource Allocation Manager (GRAM) provides the services
to actually commence a job on a fussy resource, check its
condition, and regain its results when it is over.
IV. TYPES OF GRIDS
Figure 2: Grid Architecture Computational Grid: high recital servers.
Security: A major constraint for grid computing is security. Scavenging Grid: A large number of desktops avail CPU
At the pedestal of any grid ambience, there must be methods cycles and other assets. Admittance is specified to use
to provide security, including authentication, authorization, resources to chip in in the Grid.
data encryption, etc. The Grid Security Infrastructure (GSI)
component of the Globus Toolkit provides high-end security Data Grid: Make available access to data transversely to
mechanisms [4]. compound organizations and users don't know where the data
is sited. For example, two universities doing research with
Broker: Once validated, the user will be launching an unique data [6].
application. Based on the quality, and possibly on other
parameters, the next step is to identify the appropriate Two types of grids are supported in the 2D Grid module:
resources to use within the grid out of the available ones. mesh-centered grids and cell-centered grids. With a
Although there is no broker realization provided by Globus, mesh-centered grid, the data values are stored at the corners
there is an LDAP-based information assessment. This service of the grid cells. With a cell-centered grid, data values are
is called the Grid Information Service (GIS). stored at the cell centers.
Scheduler: Once the resources have been acknowledged, the
next rational step is to plan the individual jobs to run on them.
If a set of stand-alone jobs are to be executed with no
interdependencies, then a dedicated scheduler may not be
mandatory. However, if you want to hold back a specific
resource or ensure that diverse jobs within the application run
in tandem (for instance, if they necessitate inter-process
communiqué), then a job scheduler should be used to
synchronize the execution of the jobs.
Figure 4: Types of 2D Grids Supported in GMS.
(a) Mesh-Centered Grid, (b) Cell-Centered Grid.
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International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 6, August 2012
V. GRID COMPUTING DISTINCTIVENESS The main lead of Grid computing is that it offers a customary
interface to computing and storage resources. Resources all
over the globe can be easily united together, and used by
1. Miscellany researchers ubiquitously [9]. This facilitates collaboration
2. Decentralization with other people, because resources can be joint and data
3. Vitality communal.
1. Miscellany:
VII. CONCLUSION
 Storage guiding principle
 Catalog Servers There is a natural union of grid services and Web services.
 Application Servers This convergence is stirring right now, and it is incident in all
 Diverse kinds of servers industries. It can be practical in the evolutionary philosophy
 Venture Applications of those people who are a part of VOs and are participating in
 System Services this renovation. The grid structural design and global
– Index Services principles serve a foremost role in shaping the acceptance
– Safety rate of grids in the viable world. These principles are still
– Uniqueness evolving. Grid-service conventions are non-trivial in their
– Executive Services functions; they crack some of the deep-seated issues in
distributed computing [10].
2. Decentralization:
These issues relate to the identification, creation,
 Traditional Distributed systems managed from central breakthrough, monitoring, and supervision of the duration of
admin peak [7]. state full services. More in particular, these conventions bear
very imperative distributed computing areas, as well as
 Grid computing faces challenges to exercise resources
named service instances, a two-level naming format that
graphically at scattered data centers inside an enterprise.
facilitates conventional distributed system transparencies, a
base lay down of service capabilities, including rich
3. Vitality:
innovation amenities, and unambiguously state full services
with lifetime executive capabilities.
• Grid computing, applications supple and adopt to
changing hassle.
There are at present a large number of projects and a diverse
• Apparatus of conventional application run in static
array of new and budding Grid expansion approaches being
situation
pursued. These systems range from Grid frameworks to
Ex: Components or administered from diverse nodes in a
application test beds and from collaborative milieus to set
network arrangement.
compliance mechanisms [11]. It is hard to foresee the future
• Supervision of resources in an active environment is a face
in a turf such as information technology where the technical
up to.
advances are moving in haste. Hence, it is not an easy task to
predict what will turn out to be the ‘dominant’ Grid loom.
VI. VANTAGES OF GRID COMPUTING
 Easier to join forces with other organizations. REFERENCES
[1] Foster I, Kesselman C The Grid: Blueprint for a Future
 Make improved use of obtainable hardware.
Computing Infrastructure. Morgan Kaufmann: San
 Computers functioning jointly. Francisco, CA, 1999.
 Idle computing capability is effectively used [8] [2] Computing & Information Systems
 Wide and dispersed computing gives litheness. http://www.cs.mu.oz.au/index.php
 Mainframes are idle for 40%, contribution, [3] Melbourne C.L.O.U.D.S Lab
collaboration, allotment resources gives more yield. http://www.cloudbus.org/
 Large capacity job heaps can be effectively managed [4] Introduction to Grid Computing, (IBM Redbooks)
in grid environments. http://www.redbooks.ibm.com/abstracts/
 Drop in the computing expenditure. [5] Grid computing by Joshy Joseph
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bandwidth. [6] Overview of Grid Computing
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International Journal of Advanced Research in Computer Science and Electronics Engineering
Volume 1, Issue 6, August 2012
http://net.educause.edu/ir/library/pdf/DEC0306.pdf
[7] FSU Computer Science Naveen Kumar, Enrollment No. 12070 is a final
year student pursuing B.Tech in Computer
http://www.cs.fsu.edu/research/ Science Engineering at Dronacharya College of
Engineering, Gurgaon, India. His research
[8] Gridalogy interests include Grid Computing & Robotics.
http://www.gridalogy.com/
[9] E.d.u.c.a.u.s.e: Things to be known about Grid
Computing
Rajbir Singh, Enrollment No. is a final year
http://www.educause.edu/library/resources/7-things-y student pursuing B.Tech in Computer Science
Engineering at Dronacharya College of
ou-should-know-about-grid-computing Engineering, Gurgaon, India. His research interests
include Grid Computing & Operating Systems.
[10] HowStuffWorks: How Grid Computing Works
http://computer.howstuffworks.com/grid-computing.ht
m Vaibhav Arora,
Enrollment No. 12122 is a final year student
[11] Attributes of Grid Computing pursuing B.Tech in Computer Science
Engineering at Dronacharya College of
http://docs.oracle.com/cd/E19080-01/n1.grid.eng6/817
Engineering, Gurgaon, India. His
-6117/chp1-2/index.html research interests include Grid
Computing & System Architecture.
Vikas Rohilla, Enrollment No. 12643 is a final
year student pursuing B.Tech in Computer Science
Engineering at Dronacharya College of
Engineering, Gurgaon, India. His research
interests include Grid Computing & Android OS.
Naveen Kumar*, Enrollment No. 12071 is a final
year student pursuing B.Tech in Computer
Science Engineering at Dronacharya College of
Engineering, Gurgaon, India. His research
interests include Grid Computing & Networking.
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