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Information Technology Journal
  Year: 2007 | Volume: 6 | Issue: 7 | Page No.: 978-986
DOI: 10.3923/itj.2007.978.986
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Genetic Load and Time Prediction Technique for Dynamic Load Balancing in Grid Computing

Zahida Akhtar

Gird computing is an emerging science in the field of distributed computing which involves coordinating and sharing computing, application, data, storage, or network resources across dynamic and geographically dispersed organizations. Scheduling and load balancing techniques are critical issues in grid computing for achieving good performance. The goal of load balancing is to minimize the response and execution time of a program by trying to equally spread the load on processors and maximizing their utilization. It has been proven that finding optimal schedules for the load-balancing problem is an NP-complete problem, even when the communication cost is ignorable. Genetic algorithms are a probabilistic search approach, which are founded on the ideas of evolutionary processes. They are particularly applicable to problems that are large, non-linear and possibly discrete in nature; features that traditionally add to the degree of complexity of solution. Present research aims to solve the grid load-balancing problem using Genetic Algorithms. A new Genetic Algorithm based task scheduling technique is introduced, which has been tested on a multi-node grid environment and the experimental results show that this new technique can lead to significant performance gain in various applications.
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  •    Research on Resource Scheduling Algorithm Based on ForCES Network
  •    A Novel Custom Topology Generation for Application Specific Network-on-chip Using Genetic Algorithm Optimization Technique
  •    Priority Based Load Balancing in Cloud for Data Intensive Applications
  •    The Scheduling Problem of Active Critical Chain Method
How to cite this article:

Zahida Akhtar , 2007. Genetic Load and Time Prediction Technique for Dynamic Load Balancing in Grid Computing. Information Technology Journal, 6: 978-986.

DOI: 10.3923/itj.2007.978.986






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