Grid computing is an emerging technology which is used for large computation problem. In Today’s world various complex tasks has been done in different scientific area so there is need of a lot of computational power to solve different types of complex scientific problems. Computational grid is a computer resource which is collected from multiple locations to reach a common goal. Computational grid is more geographically dispersed and heterogeneous than cluster computers. A grid is used for various purposes; a single grid is dedicated to a particular application. Grids are often constructed with general-purpose grid middleware software libraries. The book begins with an introduction that discusses the use of a Grid current trends and techniques in research scenario, It then examines the underlying action of job submission for resource sharing management using OptorSim simulator.
Early Grid Activities:
Over the past several years, there has been a lot of interest in computational Grid Computing worldwide. We also note a number of derivatives of Grid Computing, including compute grids, data grids, science grids, access grids, knowledge grids, cluster grids, terra grids, and commodity grids. As we explore careful examination of these grids, we can see that they all share some form of resources; however, these grids may have differing architectures.
In the 1990s, inspired by the availability of high-speed wide area networks and challenged by the computational requirements of new various applications, researchers began to imagine a computing infrastructure that would “provide access to computing on demand” and permit “flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources”. This vision was referred to as the Grid by analogy to the electric power grid, which provides access to power on demand, achieves economies of scale by aggregation of supply, and depends on large-scale federation of many suppliers and consumers for its effective operation.
Computation
The core functional computational requirements for grid applications are: The ability to allow for independent management of computing resources
• The ability to provide mechanisms that can intelligently and transparently select computing
• resources capable of running a user's job The understanding of the current and predicted loads on grid resources, resource
• availability, dynamic resource configuration, and provisioning Failure detection and failover mechanisms
• Ensure appropriate security mechanisms for secure resource management, access, and
• integrity
There are numerous researches have been done on resource management using scheduling algorithm in recent years, and different algorithms have their advantages and disadvantages, and there is not a perfect scheduling algorithm.
Computational Grid is a distributed and heterogeneous system that combines open, shared geographically distributed and heterogeneous resources to achieve high computational performance. Use of conventional methods becomes infeasible for resource sharing management. A new area of research which uses an optimized scheduling technique to find best possible or near optimal solution, to tackle this problem. In this research there are three grid resource scheduling algorithms named Improved Dynamic Load Balancing, Enhanced Particle Swarm Optimization (EPSO) and Fast Improved Particle Swarm Optimization with Digital pheromone has been proposed in order to provide an effective solution to the problem statement defined in the chapter 1. In this book, proposed scheduling algorithm can be evaluated by one of the two approaches: implementing the proposed algorithm in a real scheduling system and evaluation of performance or implementing the proposed algorithm in a simulator and evaluating the performance depending upon type of scheduling i.e. resource or application. Appropriate simulation tool or real system should be chosen. When a real system is to be built for evaluation of the proposed scheduling algorithms, it is very important to decide about what combination of available Grid middleware software and Grid application scheduler system can address the proposed algorithms. There are many options such as Globus, Bricks, OptorSim, SimGrid, GangSim, Arena, Alea, and, Grid (Lab) Resource Management, Nimrod-G, Sun Grid Engine, etc. In this book we have used Optorsim simulator to evaluate the performance of the system with various different scenario.
The book consists of five chapters that are organized as follows:
Chapter 1 describes the introductory part of book including introduction to the topic, problem identification, motivation, scope, principles and positioning.
Chapter 2 presents the literature survey and the research work carried out in the field of related. The chapter also covers a short history of grid evolution, standards in Grid computing and resource sharing management requirement.
Chapter 3 presents the research methodology used in this research and Optorsim simulator is used to support resource sharing management and other capabilities, such as job allocation, scheduling and balancing of jobs etc., The chapter also describes the new proposed techniques for resource sharing management using the concept of resource allocation mechanism between grids. In addition, new features can be added and incorporated easily into Optorsim for the performance evaluation used Optorsim on topics addressed in the book.
Chapter 4 presents the evaluation of the proposed technique using the simulation to demonstrate the design of grid advancement and describe the consequences of the evolution.
Chapter 5 conclude the thesis with a summary of the main findings, discussion of future research directions, final remarks and outlines possibilities for work in future.
Dr. Surendra Kumar Patel
