Survay on Job Scheduling, Load Balancing and Fault Tolerance Techniques for Computational Grids
Associate Professor, Department of Computer Science and Engineering, Atria Institute of Technology, Bangalore, Karnataka, India
- Corresponding Author:
- Jasma Balasangameshwara
Department of Computer Science and Engineering
Atria Institute of Technology, Bangalore, Karnataka, India
Tel: +91 9886 019 686
E-mail: [email protected]
Received date:November 18, 2014; Accepted date: December 15, 2014; Published date: December 25, 2014
Citation: Balasangameshwara J (2015) Survay on Job Scheduling, Load Balancing and Fault Tolerance Techniques for Computational Grids. Global J Technol Optim 6:169. doi: 10.4172/2229-8711.1000169
Copyright: © 2015 Balasangameshwara J. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Computational grid is a network of loosely coupled, heterogeneous and geographically-dispersed computers acting together to perform a large compute-intensive job. In this article, we focus on the existing approaches to grid scheduling, load balancing and fault-tolerance problems. Although grid scheduling, load balancing and fault tolerance are active research areas in grid computing, these areas have largely been and continue to be developed independent of one another each focusing on different aspects of computing. Hence, in this survey, we hope to show that robust applications that can provide efficient results can be designed by collectively considering these areas. To this end, we first provide an introduction to the motivation, grid scheduling, load balancing and fault tolerance concepts of grid computing and discuss the works that have provided significant contributions to each of these areas since its inception until 2013. We discuss their advantages, disadvantages and analyze their suitability for usage in a dynamic grid environment. We conclude that, while important advancements have been made in each of these areas individually, high performance approaches that cumulatively consider these areas still remain to be explored. We also discuss the research work that is missing and what we believe the community should be considering. To the best of our knowledge, no such survey has been conducted in the literature up to now.