A Hyper-Heuristic Approach for Efficient Resource Scheduling in Grid


  • S. Mary Saira Bhanu Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli, India
  • N.P. Gopalan Department of Computer Applications National Institute of Technology Tiruchirappalli, India


grid, hyper-heuristics, scheduling


Efficient execution of computations in grid can require mapping of tasks to processors whose performance is both irregular and time varying because of dynamic nature. The task of mapping jobs to the available computing nodes or scheduling of the jobs on the grid is a NP complete problem. The NP-hard problem is often solved using heuristics techniques. Heuristic and metaheuristic approaches tend to be knowledge rich, requiring substantial expertise in both the problem domain and appropriate heuristics techniques. To alleviate this problem the concept of Hyperheuristic was introduced. They operate on the search space of heuristics instead of candidate solutions and can be applied to any optimization problem. This paper emphasizes the use of Hyper-heuristics built on top of hybridized Metaheuristics to efficiently and effectively schedule jobs onto available resources in a grid environment thus resulting in an optimal schedule with minimum makespan.


Moreno, R., Alonso-Conde A.B, Job Scheduling and Resource Management Techniques in Dynamic Grid Environments In et al., F.F.R., ed.: Across Grids 2003, Volume 2970 of Lecture Notes in computer science, Springer, pp : 25 - 32, 2004.

Marek Mika, Grzegorz Waligora and Jan Weglarz, A Meta-Heuristic Approach to scheduling Workflow jobs on a Grid, Grid resource management: state of the art and future trends, ISBN : 1-4020- 7575-8 , Kluwer Academic Publishers, Norwell, MA, USA; pp : 295 - 318, 2004.

Ajith Abraham, Rajkumar Buyya and Baikunth Nath, Nature's Heuristics for Scheduling jobs on Computational Grids, International Conference on Advanced Computing and Communications 2000, 2000.

Tracy D.Braun, Howard Jay Siegel and Noah Beck, A Comparison of eleven static heuristics for mapping a class of tasks to heterogeneous Distributed Computing System, Journal of Parallel and Distributed Computing Vol:61, pp: 810 - 837, 2000. http://dx.doi.org/10.1006/jpdc.2000.1714

Edmund Burke, Yuri Bykov, James Newall and Sanja Petrovic, A Time-Predefined Approach to Course Timetabling, Yugoslav Journal of Operations Research, 13(2), pp : 139-151, 2000.

Jarek Nabrzyski, Jennifer M. Schopf and JanWeglarz, Grid Resource Management - State of Art and Future Trends, ISBN : 1-4020-7575-8, Kluwer Academic Publishers, Norwell, MA, USA, 2004.

Peter Kowling, Graham Kendall and Eric Soubeiga, Hyper - heuristics : A tool for rapid prototyping in scheduling and optimization, LNCS 2279, Applications of Evolutionary Computing : Proceedings of EvoCop2002, Kinsale, Ireland, 2002.

Ian Foster, Carl Kesselman and Steven Tuecke, The Anatomy of the Grid: Enabling Scalable Virtual Organizations, International J. Supercomputer Applications 15(3), 2001.

Mona Aggarwal, Robert D. Kent and Alionne Ngom, Genetic algorithm based scheduler for Computational Grids, International Symposium on High performance Computing Systems and Applications (HPCS'05), IEEE, 2005. http://dx.doi.org/10.1109/HPCS.2005.27

Vincenzo Di Martino, SubOptimal Scheduling in a Grid using Genetic Algorithms, Parallel and nature-inspired computational paradigms and applications, Elsevier Science Publishers pp: 553 - 565, 2004.

Javier Carretero and Fatos Xhafa, Use of Genetic Algorithms for Scheduling Jobs in Large scale Grid applications, Okio Technologies IR Ekoonominis Vvstymas, Technological and Economic development of Economy, Vol XII, No.1 pp 11-17.

Andrew J. Page and Thomas J. Naugton, Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing, IEEE International Parallel and Distributed Processing Symposium (IPDPS'05), 2005. http://dx.doi.org/10.1109/IPDPS.2005.184

Burke, E K. Kendall, G., Newall, J., Hart E., Ross,P., Schulnenburg, Handbook of Metaheuristics, Chapter 16, Hyper-heuristics: an emerging direction in modern search technology, pp 457-474, Kluwer Academic Publishers, 2003.

Lingyun Yang, Jennifer M. Schopf and Ian Foster, Conservative Scheduling: Using predictive variance to improve scheduling decisions in Dynamic Environments, SuperComputing 2003, November 15-21, Phoenix, AZ, USA.

Juan Antonio Gonzalez, Maria Serna and Fatos Xhafa,2007, A Hyper-heuristic for scheduling independent jobs in Computational Grids, International conference on software and data technologies, ICSOFT (2007).

Stefka Fidanova, Simulated annealing for Grid Scheduling problem,International Symposium on Modern Computing (JVA'06) IEEE, 2006.



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