Grid Computing

Grid Designing

Abstract: In an increasing number of scientific disciplines, large data

collections are emerging as important community resources. Grid

computing has emerged as an important new field, distinguished from

conventional distributed computing by its focus on large-scale resource

sharing, innovative applications, and, in some cases, high performance

orientation. The foundation of a grid solution design is typically built

upon an existing infrastructure investment. However, a grid solution

does not come to fruition by simply installing software to allocate

resources on demand. The grid solutions are adaptable to meet the needs

of various business problems only because differing types of grids are

designed to meet specific usage requirements and constraints. Different

topologies are designed to meet varying geographical constraints and

network connectivity requirements. The success of a grid solution is

heavily dependant on the amount of thought the IT architect puts into the

solution design. Harnessing these new technologies effectively will

transform scientific disciplines ranging from high-energy physics to the

life sciences. . In this paper, we discuss designing of grid along with

underlying topologies and models that allow for grid computing to work.

Keyword: Virtual organization (VO), Globus tool kit, Grid Security

Infrastructure (GSI).

1 Introduction to grid computing

A grid is a collection of machines, sometimes referred to as

nodes, resources, members, donors, clients, hosts, engines, and

many other such terms. They all contribute any combination of

resources to the grid as a whole. Grid computing is an emerging

computing model that provides the ability to perform higher

throughput computing by taking advantage of many networked

computers to model a virtual computer architecture that is able to

distribute process execution across a parallel infrastructure. Grids

use the resources of many separate computers connected by a

network to solve large-scale computation problems [1]. Grids

provide the ability to perform computations on large data sets, by

breaking them down into many smaller ones, or provide the

ability to perform many more computations at once than would

be possible on a single computer, by modeling a parallel division

of labor between processes. Grid computing involves sharing

heterogeneous resources located in different places belonging to

different administrative domains over a network using open

standards. In short, it involves virtualizing computing resources.

Grid computing is often confused with cluster computing. The

key difference is that a cluster is a single set of nodes sitting in

one location, while a Grid is composed of many clusters and

other kinds of resources. Grid computing reflects a conceptual

framework rather than a physical resource. The Grid approach is

utilized to provision a computational task with administrativelydistant

resources. The focus of Grid technology is associated

with the issues and requirements of flexible computational

provisioning beyond the local administrative domain. A Grid

environment is created to address resource needs. The use of that

resource is usually characterized by its availability outside of the

context of the local administrative domain. This 'external

provisioning' approach entails creating a new administrative

domain referred to as a Virtual Organization (VO) with a distinct

and separate set of administrative policies The context for a Grid

'job execution' is distinguished by the requirements created when

operating outside of the home administrative context. Grid

technology is employed to facilitate formalizing and complying

with the Grid context associated with your application execution

[2].

2. Building grid architecture

Once the functional and non-functional requirements are known,

the IT architect should readily be able to select the type of grid

and the best topology required to satisfy the majority of the

business requirements. When armed with this information, the

high-level grid design will be easier to complete, and by

leveraging the use of known grid types and topologies,

articulating the solution design will require much less effort. It is

important to focus on starting small and to begin building the

basic framework of the design. Rather than setting out to build

the desired end state