Resource Allocation in Grid Computing Environment Using Genetic–Auction Based Algorithm

Resource Allocation in Grid Computing Environment Using Genetic–Auction Based Algorithm

Kuppani Satish (Department of Computer Science and Engineering, Sri Venkateswara University, Tirupati, India) and A. Rama Mohan Reddy (Department of Computer Science and Engineering, Sri Venkateswara University, Tirupati, India)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/IJGHPC.2018010101
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The main core functionality of Grid Computing is resource allocation and scheduling. With the idea of genetic algorithms and microeconomics, it is proposed a Resource allocation method called a genetic-auction based algorithm [GAAB]. This algorithm contains two modules, auction module and genetic approach. Auction module find outs resource-trading price between resource provider and resource buyer, and the resource allocation carried out by Genetic algorithm by considering both time and cost constraints simultaneously. In this article, evaluations are made in the simulation environment and the results show the effectiveness of the proposed model.
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1. Introduction

Grid computing is one of the emerging fields in parallel and distributed computing. Grids are evolving as the compromising future generation of computational platforms for executing large-scale resource concentrated applications arising in the field of science, engineering, and commerce (Foster and Kesselman, 1999; Abraham et al., 2000; Xhafa et al., 2007). It supports the heterogeneous resources by the creation of virtual organisations and enterprises. These virtual organisations enable the selection, sharing, aggregation and exchange of information between heterogeneous resources. The customer can access grid resources by maintaining a grid portal like Globus (Foster and Kesselman, 1997) and Legion (Grimshaw and Wulf, 1997) and each resource owner share their resources by running a grid portal too. Different customers’ demands different resources, different resources have different capabilities and availabilities based on their policies, which are selected. At any point of time, the resources may enter or exit from the grid. On the other side, customers with varying resources (Babu and Krishna, 2014) can enter in to the grid. As a result, the environment of the grid is highly dynamic, uncontrollable, and heterogeneous across different domains.

The traditional methods cannot be applied simply to the grid resource management because they adopt total control over requests and resources. Meanwhile the grid resources are belonging to different domains and are distributed in different geographical regions so decentralized method is an appropriate solution for resource management in grid. A suitable resource management for grid exploits the resources capability effectively and satisfies the customer requests. In the past few years, the growth of market based resource management takes place.

The two main characteristics of supportable market-based resource management is to allow resource consumers and resource suppliers to make independent decisions for scheduling, and both events of providers and customers must have adequate rewards to stay and play in the market (Xiao et al., 2008). Two groups of industry-based designs that are used for grid resource management (Babu et al., 2014) are public auction model and product market models. In public auction model, each provider and consumer acts individually and they agree independently on the cost level. In industrial design, providers specify their resource cost and charge users according to the quantity of resource they consume.

In this paper job, scheduling method (Reddy et al., 2014) is suggested with the knowledge of Microeconomics and Genetic technology by taking both time limit and cost. It determines the trading price based on auction model between resource customers and resource suppliers, and then gets a resource scheduling solution based on the cost using GA (Genetic algorithm) (Cheng et al., 2004).

The rest of the paper is organized as follows. In section 2, it deals with related work and in section 3 deals with problem formulation, section 4 deals with the auction model for resource pricing., In section 5 deals with proposed method as a resource allocation process by using GAAB. In section 6 evaluate the results and finally section 7 concludes this work.

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