Load Balancing in Grid Computing Using AI Techniques
Work load and resource management are two important factors that have to manage across the grid environment. To increase the overall efficiency of grid based infrastructure the work load across the grid environment has to manage. Hence the work load must
- PDF / 661,865 Bytes
- 12 Pages / 439.37 x 666.142 pts Page_size
- 100 Downloads / 218 Views
Abstract Work load and resource management are two important factors that have to manage across the grid environment. To increase the overall efficiency of grid based infrastructure the work load across the grid environment has to manage. Hence the work load must be evenly scheduled across the grid nodes so that grid resources can be properly exploited. The technique that we have investigated in this paper is based upon the combination of genetic algorithms which is an evolutionary algorithm and artificial neural networks. Both of these techniques are applied for local grid load balancing. Genetic algorithm selects the optimal set of jobs for assigning to the grid nodes which overall minimizes the total execution time. Afterwards when optimal set of jobs is selected they are assigned to artificial neural network which selects the minimum loaded grid processor for further processing of this optimal set of jobs. We compare our proposed technique with the already existing strategies for load balancing like random algorithm, round robin algorithm, decreasing time algorithm and least connection algorithm. Results shows that our strategy gives optimal results in terms of overall time efficiency. So we can overall conclude that GA’s and ANN’s increase overall efficiency of job scheduling especially in case where the tasks coming for scheduling and processing nodes are continuously increasing. Keywords Genetic algorithm
Artificial neural network Load balancing
N. T. Inam (&) M. Daud Awan S. S. Afzal Department of Computer Science, Preston University, Islamabad, Pakistan e-mail: [email protected]
S.-S. Yeo et al. (eds.), Computer Science and its Applications, Lecture Notes in Electrical Engineering 203, DOI: 10.1007/978-94-007-5699-1_93, Springer Science+Business Media Dordrecht 2012
917
918
N. T. Inam et al.
1 Introduction With the advent of internet we encounter an explosion of information. To convert this huge plethora of information into meaningful data, we need increasingly reciprocally faster processing. Hence this faster processing can be limit to single processing unit and can be expanded to variety of processing types like multiple processing units, distributed processing units and parallel processing. Distributed computing gives one of the modern form of computing known as grid computing. It is a network in which resource of a particular computer can be used by any other node which is united to that particular network. If we look then we can find that sharing resources has been an important fact over the internet or networks [1, 2] There are number of resources like computing powers, storage capacities, processing powers that are present in grid environment, that can be use by all those people who are member of grid environment. A grid computing design can have several computers running on a network having same hardware or software or it can have multiple diverse forms of hardware or software even one can imagine. In grid computing all systems are incorporated in such a way that someone is us
Data Loading...
