Nature's heuristics for scheduling jobs on Computational Grids

In this paper, we propose an adaptive and dynamic scheduling method, called most fit task first (MFTF), for a class of computational grids, which are characterized by heterogeneous computing nodes and dynamic task arrivals. Some existing static scheduling methods assume that tasks arrive statically and may not perform well in the case of dynamic task arrivals. Our method can get stable task execution times whether tasks arrive statically or dynamically. We compare the task execution time with other methods to show the performance of the scheduling method.

[1]  Larry Rudolph,et al.  Parallel Job Scheduling: Issues and Approaches , 1995, JSSPP.

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Henri Casanova,et al.  Simgrid: a toolkit for the simulation of application scheduling , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[4]  Rajkumar Buyya,et al.  The Grid: International Efforts in Global Computing , 2000 .

[5]  Baikunth Nath,et al.  A Genetic Algorithm for Scheduling Independent Jobs on Uniform Machines with Multiple Objectives , 1998 .

[6]  Amitava Majumdar.  Parallel performance study of Monte Carlo photon transport code on shared-, distributed-, and distributed-shared-memory architectures , 2000, Proceedings 14th International Parallel and Distributed Processing Symposium. IPDPS 2000.

[7]  David Abramson,et al.  An Economy Driven Resource Management Architecture for Global Computational Power Grids , 2000, PDPTA.

[8]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[9]  Ajith Abraham,et al.  Parallel Machine Scheduling using Genetic Algorithms , 2000 .

[10]  Satoshi Matsuoka,et al.  Overview of a performance evaluation system for global computing scheduling algorithms , 1999, Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469).

[11]  David Abramson,et al.  High performance parametric modeling with Nimrod/G: killer application for the global grid? , 2000, Proceedings 14th International Parallel and Distributed Processing Symposium. IPDPS 2000.

[12]  John Shalf,et al.  The Cactus Worm: Experiments with Dynamic Resource Discovery and Allocation in a Grid Environment , 2001, Int. J. High Perform. Comput. Appl..

[13]  David Abramson,et al.  Grid Resource Management, Scheduling and Computational Economy , 2000 .

[14]  Nancy Wilkins-Diehr,et al.  Studying protein folding on the Grid: experiences using CHARMM on NPACI resources under Legion , 2004, Concurr. Comput. Pract. Exp..

[15]  Satish K. Tripathi,et al.  Static and Dynamic Processor Scheduling Disciplines in Heterogeneous Parallel Architectures , 1995, J. Parallel Distributed Comput..

[16]  R. F. Freund,et al.  Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems , 1999, Proceedings. Eighth Heterogeneous Computing Workshop (HCW'99).

[17]  Ian T. Foster,et al.  Grid information services for distributed resource sharing , 2001, Proceedings 10th IEEE International Symposium on High Performance Distributed Computing.

[18]  Daniel Costa,et al.  An Evolutionary Tabu Search Algorithm And The NHL Scheduling Problem , 1995 .

[19]  Ajith Abraham,et al.  Hybrid Heuristics for Optimal Design of Artificial Neural Networks , 2001 .

[20]  Woodie C. Flowers,et al.  A genetic algorithm for resource-constrained scheduling , 1996 .

[21]  Santosh K. Shrivastava,et al.  A System for Fault-Tolerance Execution of Data and Compute Intensive Programs over a Network of Workstations , 1996, Euro-Par, Vol. I.

[22]  Richard Wolski,et al.  The network weather service: a distributed resource performance forecasting service for metacomputing , 1999, Future Gener. Comput. Syst..

[23]  Francine Berman,et al.  A Decoupled Scheduling Approach for the GrADS Program Development Environment , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[24]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[25]  Ian T. Foster,et al.  Globus: a Metacomputing Infrastructure Toolkit , 1997, Int. J. High Perform. Comput. Appl..

[26]  Fred W. Glover,et al.  A user's guide to tabu search , 1993, Ann. Oper. Res..

[27]  Xin Yao,et al.  A new simulated annealing algorithm , 1995, Int. J. Comput. Math..

[28]  Ming Wu,et al.  A general self-adaptive task scheduling system for non-dedicated heterogeneous computing , 2003, 2003 Proceedings IEEE International Conference on Cluster Computing.

[29]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.

[30]  Baikunth Nath.  A Hybrid GA-SA Algorithm for Flowshop Scheduling Problems , 1997 .

[31]  Erick Cant,et al.  Designing Efficient And Accurate Parallel Genetic Algorithms , 1999 .

[32]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[33]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[34]  Francisco Vilar Brasileiro,et al.  Trading Cycles for Information: Using Replication to Schedule Bag-of-Tasks Applications on Computational Grids , 2003, Euro-Par.

[35]  Miron Livny,et al.  Harnessing the Capacity of Computational Grids for High Energy Physics , 2000 .