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Some Basic Concepts of Grid Computing Mohsen Gerami Abstract—Grid technologies are the convergence of distributed and parallel computing and offer high levels of computational, storage and network capacity. Grid computing named after the similarity with electricity grid. It consist of a number of resources interrelated during a network. Grid computing supports the sharing of distributed resources, and it enables scientists and engineering professionals to solve large scale computing problems. This paper surveys the architecture, types, characteristics and security of grid computing. Index Terms—Grid Computing, Implementation, Architecture, Security;

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1 INTRODUCTION

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rid computing is a model of distributed computing that uses geographically and administratively disparate resources. In grid computing, individual users can access computers and data transparently, without having to consider location, operating system, account administration, and other details. In grid computing, the details are abstracted, and the resources are virtualized [1]. Grid is a software environment that makes it possible to share disparate, loosely coupled IT resources across organizations and geographies. Using a grid, your IT resources are freed from their physical boundaries and offered as services. These resources include almost any IT component – computer cycles, storage spaces, databases, applications, files, sensors or scientific instruments. In grid computing, resources can be dynamically provisioned to your users or applications that need them. Resources can be shared within a workgroup or department, across different organizations and geographies or outside your enterprise [2]. The idea of grid computing originated with Ian Foster, Carl Kesselman and Steve Tuecke. They got together to develop a toolkit to handle computation management, data movement, storage management and other infrastructure that could handle large grids without restricting themselves to specific hardware and requirements. The technique is also exceptionally flexible [3]. A grid can provide significant processing power for users with extraordinary needs. Grids address two distinct but related goals: providing remote access to IT assets, and aggregating processing power. The most obvious resource included in a grid is a processor, but grids also encompass sensors, data-storage systems, applications, and other resources. A grid might coordinate scientific instruments in one country with a database in another and processors in a third. From a user’s perspective, these resources function ————————————————

• Dr. Mohsen Gerami, Assistant Professor of Information and communication Technology, Faculty of Applied Science of Post and Communications, Tehran, Iran.

as a single system—differences in platform and location become invisible. A grid can provide significant processing power for users with extraordinary needs. Grids make research projects possible that formerly were impractical or unfeasible due to the physical location of vital resources. With grids, programs previously hindered by constraints on computing power become possible. Grids can provide unprecedented access to facilities and tools involve a high level of complexity [4].

2 BENEFITS AND DIFFERENT TYPES OF GRID COMPUTING

Grid computing appears to be a promising trend for three reasons: (1) its ability to make more cost-effective use of a given amount of computer resources, (2) as a way to solve problems that can't be approached without an enormous amount of computing power, and (3) because it suggests that the resources of many computers can be cooperatively and perhaps synergistically harnessed and managed as a collaboration toward a common objective. In some grid computing systems, the computers may collaborate rather than being directed by one managing computer [5]. With grid computing, your far-flung and disparate IT resources can act as a single 'virtual data centre'. The virtualisation of your heterogeneous IT resources means these resources are available when and where you need them. It allows you to provision applications and allocate capacity among business groups that are geographically and organisationally dispersed [7].Through the use of this technology you can: • Enable secure, real-time collaboration among global teams, including partners and suppliers outside your company • Deploy resources for new initiatives rapidly • Accelerate new product development and improve time to market • Reduce IT costs and increase return on investment • Handle peaks and troughs in demand by provisioning resources where needed

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According to Baker [6] four main aspects characterize a Grid. These characteristics may be described as follows: • Multiple administrative domains and autonomy. Grid resources are geographically distributed across multiple administrative domains and owned by different organizations. The autonomy of resource owners needs to be honored along with their local resource management and usage policies. • Heterogeneity. A Grid involves a multiplicity of resources that are heterogeneous in nature and will encompass a vast range of technologies. • Scalability. A Grid might grow from a few integrated resources to millions. This raises the problem of potential performance degradation as the size of Grids increases. Consequently, applications that require a large number of geographically located resources must be designed to be latency and bandwidth tolerant. • Dynamicity or adaptability. In a Grid, resource failure is the rule rather than the exception. In fact, with so many resources in a Grid, the probability of some resource failing is high. Resource managers or applications must tailor their behavior dynamically and use the available resources and services efficiently and effectively. There are many different types of grid computing. The four common types of grid have described below [8]. 1. Clusters – grid computing splits up a single application to run on many computers or processors at the same time. Clusters of different types of computers or grid computers provide the hardware to run these split up applications. This is usually a first step in the use of grids. 2. Data grids – this is a way to access geographically distributed data resources. Data grids provide a way to collaborate and share data and information resources. Many firms with dispersed research groups were the first to use data grids, such as pharmaceutical firms. 3. Enterprise Grids – provide a way to share compute and data or information resources within a single firm, usually linking together several different operations and locations within the firm. 4. Partner Grids – create a way to share compute and data resources between at least two unrelated firms, where one firm may be a supplier to another or a collaborator with another..

3 IMPLEMENTING GRID COMPUTING Grid computing is a powerful IT model that helps you improve collaboration within and outside your organization. When done correctly, a grid implementation enables you to virtualizes your resources and share them as services across your enterprise [9]. Grid computing techniques can be used to create very different types of grids, adding flexibility as well as power by using the resources of multiple machines. An equipment grid will use a grid to control a piece of equipment, such as a telescope, as well as analyze the data that equipment collects. A data grid, however, will primarily manage large amounts of information, allowing users to share access. Grid computing is similar to cluster computing, but there

are a number of distinct differences. In a grid, there is no centralized management; computers in the grid are independently controlled, and can perform tasks unrelated to the grid at the operator's discretion. The computers in a grid are not required to have the same operating system or hardware. Grids are also usually loosely connected, often in a decentralized network, rather than contained in a single location, as computers in a cluster often are [10]. Grid computing requires the use of software that can divide and farm out pieces of a program to as many as several thousand computers. Grid computing can be thought of as distributed and large-scale cluster computing and as a form of network-distributed parallel processing. It can be confined to the network of computer workstations within a corporation or it can be a public collaboration (in which case it is also sometimes known as a form of peerto-peer computing). A number of corporations, professional groups, university consortiums, and other groups have developed or are developing frameworks and software for managing grid computing projects. The European Community (EU) is sponsoring a project for a grid for high-energy physics, earth observation, and biology applications. In the United States, the National Technology Grid is prototyping a computational grid for infrastructure and an access grid for people. Sun Microsystems offers Grid Engine software. Described as a distributed resource management (DRM) tool, Grid Engine allows engineers at companies like Sony and Synopsys to pool the computer cycles on up to 80 workstations at a time. (At this scale, grid computing can be seen as a more extreme case of load balancing.) [5].

4 GRID ARCHITECTURE Traditional distributed technologies do not support an integrated approach to the wide variety of required services and resources, and they lack the flexibility and control needed to enable the type of resource sharing necessary. There is a need to define a grid software infrastructure to support the heterogeneous aspects of the grid. The grid infrastructure is based on a standard open architecture which facilitates extensibility, interoperability, portability and code sharing. This architecture organizes components into layers, as shown in Fig 1. Components within each layer share common characteristics, but can build on the capabilities and behaviors of any lower layer.

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Fig 1: Layers of Grid Architecture [11]

1. Fabric Layer The fabric layer comprises the resources in the grid. This resource can be either a logical (such as a distributed _le system, computer cluster or distributed Figure 1: Layers of Grid Architecture [11] computer pool) or a physical resource (such as a computational resource, storage system, catalogue, network resource or sensor). This layer provides the lowest level of access to actual native resources, and implements the low-level mechanisms that allow those resources to be accessed and used. More specifically, those mechanisms must include at least state enquiry and resource management mechanisms, each of which must be implemented for a large variety of local systems. 2. Connectivity layer The connectivity layer provides the core communication and authentication protocols required for grid-specific network transactions. These protocols provide cryptographically secure mechanisms by which to verify the identified grid users and resources. 3. Resource Layer This layer builds on the connectivity layer in order to implement protocols that enable the use and sharing of individual resources such as the Grid Resource Access and Management protocol (GRAMP) used to allocate and monitor resources. More specifically, two fundamental components of this layer are information protocols, for querying the state of a resource by calling fabric layer functions to control and access resources, and management protocols, used to negotiate access to a shared resource. 4. Collective Layer This layer provides protocols such as the Grid Resource Information Protocol (GRIP) [11] for interacting across collections of resources. In other words, it focuses on the coordination of multiple resources. It includes directory, coal location, scheduling, brokerage, monitoring and diagnostics, data replication, software discovery, community accounting and payment services. 5. Application Layer This is the final layer in grid architecture, and contains the user applications that operate in a grid environment. It includes languages and frameworks. These frameworks may themselves define protocols such as Simple Workflow Access Protocol (SWAP) [12], services, and/or an Application Program Interface (API) [13].

5 SECURITY Everyone involved in grid computing has concerns regarding security, although the specifics of the concerns may differ. Contributors of resources are concerned that the privacy and integrity of their systems be ensured; users of the systems are concerned that the integrity of their applications, data, and results be maintained; systems administrators are concerned that the resources be made available only to approved users. Numerous mechanisms address these concerns, including creating a “sandbox” in

which the grid processes run. Jobs in the sandbox are limited in the interactions they can have with the client system and can read and write only to files within the sandbox. Digital signatures, checksums, and encryption can be used where needed [1]. While scalability, performance and heterogeneity are desirable goals for any distributed system, the characteristics of computational grids lead to security problems that are not addressed by existing security technologies for distributed systems. For example, parallel computations that acquire multiple computational resources introduce the need to establish security relationships not simply between a client and a server, but among potentially hundreds of processes that collectively span many administrative domains. Furthermore, the dynamic nature of the grid can make it impossible to establish trust relationships between sites prior to application execution. Finally, the inter domain security solutions used for grids must be able to interoperate with, rather than replace, the diverse intra domain access control technologies inevitably encountered in individual domains [15].

6 CONCLUSION Grid computing presents a new concept to IT infrastructures. Grid computing supports various programming models and reduces the total cost of ownership. Grid computing improves business agility by reducing time to carry results, and improves collaboration and sharing of resources. Usually, grids are classified by the kind of solutions that they best address.Grid computing facilitates use of heterogeneous hardware and software resources. Grid computing is a developing area of computing and Grid’s standards and grid’s technology are still developing.

REFERENCES [1]

Michael P. Cummings and Jeffrey C. Husakamp, 2005, Grid Computing, Educause R e v i e w [2] http://h71028.www7.hp.com/enterprise/w1/en/technologies/gridcomputing-what-is-grid-computing.html [3] http://www.wisegeek.com/what-is-grid-computing.htm [4] Educause Learning Initiative (ELI), 2006, 7 things you should know about Grid Computing, www.educause.edu/eli, January 2006 [5] http://searchdatacenter.techtarget.com/sDefinition/0,,sid80_gci77315 7,00.html [6] Baker Mark, Rajkumar Buyya, and Domenico Laforenza, Grids and Grid technologies for wide-area distributed computing, Softw. Pract. Exper. 2002; (in press) (DOI: 10.1002/spe.488) [7] http://h71028.www7.hp.com/enterprise/w1/en/technologies/gridcomputing-why-grid.html [8] Cohen Robert B., 2008, Cluster and Grid Computing in Japan: Today and in 2010 [9] http://h71028.www7.hp.com/enterprise/w1/en/technologies/gridcomputing-solutions.html [10] http://www.wisegeek.com/what-is-grid-computing.htm [11] I. Foster, C. Kesselman, and S. Tuecke, \The anatomy of the grid – enabling scalable virtual organizations," International Journal of Super-

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computer Applications, vol. 15, p. 2001, 2001. [12] K. Swenson, \Simple workow access protocol (swap)," http://www.isr.uci.edu/events/twist/wisen98/presentations/Swens on/. [13] Mai Ahmad AL-Fawair, 2009, A Framework for Evolving Grid Computing Systems, Faculty of Technology De Montfort University United Kingdom, England [14] Ian Foster, Carl Kesselman, Gene Tsudik, Steven Tuecke, A Security Architecture for Computational Grids,