LGR: The New Genetic Based Scheduler for Grid Computing Systems

The computational grid provides a promising platform for the deployment of various high-performance computing applications. In computational grid, an efficient scheduling of task onto the processors that minimizes the entire execution time is vital for achieving a high performance. Solving this problem is very hard and many attempts have been made to solve the problem. Using classical algorithms, With regard to the complexity of this problem, is not the good way; so the indefinite method acts better than classical method. Evolutionary algorithms are the best choice for solving this hard problem. In this paper, contrary to prior ways, the new string representation has been used, communication costs has not been ignored and presents as a major factor for reaching to optimum solution.

[1]  Mitsuo Gen,et al.  A comparison of multiprocessor task scheduling algorithms with communication costs , 2008, Comput. Oper. Res..

[2]  Francine Berman,et al.  High-performance schedulers , 1998 .

[3]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[4]  Selim G. Akl,et al.  Scheduling Algorithms for Grid Computing: State of the Art and Open Problems , 2006 .

[5]  Ronald L. Rivest,et al.  Introduction to Algorithms, Second Edition , 2001 .

[6]  Mahdi Mahmoodi,et al.  A novel intelligent method for task scheduling in multiprocessor systems using genetic algorithm , 2006, J. Frankl. Inst..

[7]  Nimal Nissanke,et al.  Probabilistic performance analysis in multiprocessor scheduling , 2002 .

[8]  David Abramson,et al.  Economic models for resource management and scheduling in Grid computing , 2002, Concurr. Comput. Pract. Exp..

[9]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[10]  Sivarama P. Dandamudi,et al.  An Efficient Adaptive Scheduling Scheme for Distributed Memory Multicomputers , 2001, IEEE Trans. Parallel Distributed Syst..

[11]  Daniel Gajski,et al.  A Programming Aid for Message-passing Systems , 1987, PPSC.

[12]  Tao Yang,et al.  DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors , 1994, IEEE Trans. Parallel Distributed Syst..

[13]  Zhong Yi-wen,et al.  A Hybrid Genetic Algorithm for Tasks Scheduling in Parallel Multiprocessor Systems , 2003 .

[14]  Selim G. Akl,et al.  A Joint Data and Computation Scheduling Algorithm for the Grid , 2007, Euro-Par.

[15]  Jian-Gang Yang,et al.  A genetic algorithm for tasks scheduling in parallel multiprocessor systems , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[16]  Marin Golub,et al.  Scheduling Multiprocessor Tasks with Genetic Algorithms , 2019 .

[17]  K. Mani Chandy,et al.  A comparison of list schedules for parallel processing systems , 1974, Commun. ACM.

[18]  Imtiaz Ahmad,et al.  Multiprocessor Scheduling in a Genetic Paradigm , 1996, Parallel Comput..

[19]  Nirwan Ansari,et al.  A Genetic Algorithm for Multiprocessor Scheduling , 1994, IEEE Trans. Parallel Distributed Syst..

[20]  Willard Korfhage,et al.  Process scheduling using genetic algorithms , 1995, Proceedings.Seventh IEEE Symposium on Parallel and Distributed Processing.

[21]  Jesús Labarta,et al.  Performance-driven processor allocation , 2000, IEEE Transactions on Parallel and Distributed Systems.

[22]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[23]  Daniel Gajski,et al.  Hypertool: A Programming Aid for Message-Passing Systems , 1990, IEEE Trans. Parallel Distributed Syst..

[24]  Nirwan Ansari,et al.  Efficient multiprocessor scheduling based on genetic algorithms , 1990, [Proceedings] IECON '90: 16th Annual Conference of IEEE Industrial Electronics Society.