Abstract
A grid computing environment is a parallel and distributed system that brings together various computing capacities to solve large computational problems. Task scheduling is a critical issue for grid computing; in task scheduling, tasks are mapped onto system processors with the aim of achieving good performance in terms of minimizing the overall execution time. In previous studies, there have been several approaches to solving the task-scheduling problem by genetic algorithms, which is a random search technique that is inspired by natural biological evolution. This study presents a genetic algorithm for solving the problem of task scheduling with two main ideas: a new initialization strategy to generate the first population and new genetic operators based on task–processor assignments to preserve the good characteristics of the found solutions. Our proposed algorithm is implemented and evaluated using a set of well-known applications in our specifically defined system environment. The experimental results show that the proposed algorithm outperforms other popular algorithms in a variety of scenarios with several parameter settings.
Similar content being viewed by others
References
Arabnejad H, Barbosa JG (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans Parallel Distrib Syst 25(3):682–694
Arabnia HR (1990) A parallel algorithm for the arbitrary rotation of digitized images using process-and-data-decomposition approach. J Parallel Distrib Comput 10(2):188–193
Arabnia HR, Oliver MA (1989) A transputer network for fast operations on digitised images. Comput Graph Forum 8(1):3–12
Bhandarkar SM, Arabnia HR (1995) The REFINE multiprocessor: theoretical properties and algorithms. Parallel Comput 21(11):1783–1806
Culler D, Singh J, Gupta A (1998) Parallel computer architecture: a hardware/software approach. Morgan Kaufmann Publisher, San Francisco
Chitra P, Rajaram R, Venkatesh P (2011) Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems. Appl Soft Comput 11(2):2725–2734
Choudhury P, Chakrabarti PP, Kumar R (2012) Online scheduling of dynamic task graphs with communication and contention for multiprocessors. IEEE Trans Parallel Distrib Syst 23(1):126–133
Falzon G, Li M (2012) Enhancing genetic algorithms for dependent job scheduling in grid computing environments. J Supercomput 62(1):290–314
Freund RF, Siegel HJ (1993) Guest editor’s introduction: heterogeneous processing. Computer 26(6):13–17
Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Hwang K (1993) Advanced computer architecture: parallelism, scalability, programmability. McGraw-Hill Inc, New York
Hou ESH, Ansari N, Ren H (1994) A genetic algorithm for multiprocessor scheduling. IEEE Trans Parallel Distrib Syst 5(2):113–120
Hyunjin K, Sungho K (2011) Communication-aware Task scheduling and voltage selection for total energy minimization in a multiprocessor system using ant colony optimization. Inf Sci 181(18):3995–4008
Kwok YK, Ahmad I (1999) Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput Surv 31(4):406–471
Leighton FT (1992) Introduction to parallel algorithms and architectures: arrays, trees, hypercubes. Morgan Kaufmann, San Mateo
Liou J, Palis MA (1996) An efficient task clustering heuristic for scheduling DAGs on multiprocessors. In: Proceeding of workshop on resource management, symposium of parallel and distributed processing, pp 152–156
Liu H, Abraham A, Snášel V, McLoone S (2012) Swarm scheduling approaches for work-flow applications with security constraints in distributed data-intensive computing environments. Inf Sci 192:228–243
Jiang YS, Chen WM (2013) Task scheduling in grid computing environments. In: Proceedings of the Seventh International Conference on Genetic and Evolutionary Computing, pp 23–32
Mirabi M (2011) Ant colony optimization technique for the sequence-dependent flowshop scheduling problem. Int J Adv Manufact Technol 55(1–4):317–326
Omara FA, Arafa MM (2010) Genetic algorithms for task scheduling problem. J Parallel Distrib Comput 70(1):13–22
Rewini HE, Lewis T, Ali H (1994) Task scheduling in parallel and distributed systems. Prentice Hall, New Jersey
Rahman M, Hassan R, Ranjan R, Buyya R (2013) Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurr Comput Pract Exp 25(13):816–1842
Tang X, Li K, Liao G, Li R (2010) List scheduling with duplication for heterogeneous computing systems. J Parallel Distrib Comput 70(4):323–329
Tao Q, Chang HY, Yi Y, Gu CQ, Li WJ (2011) A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow. Comput Oper Res 38(5):824–836
Topcuoglu H, Hariri S, Wu M-Y (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274
Wen Y, Xu H, Yang J (2011) A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system. Inf Sci 181(3):567–581
Wu AS, Yu H, Jin S, Lin K, Schiavone G (2004) An incremental genetic algorithm approach to multiprocessor scheduling. IEEE Trans Parallel Distrib Syst 15(9):824–834
Yu H (2008) Optimizing task schedules using an artificial immune system approach. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp 151–158
Zarrabi A, Samsudin K (2014) Task scheduling on computational grids using gravitational search algorithm. Cluster Comput 17(3):1001–1011
Author information
Authors and Affiliations
Corresponding author
Additional information
This paper is an extended version of work published in [18].
This work is partially supported by National Science Council under the Grant NSC 103-2221-E-011-113.
Rights and permissions
About this article
Cite this article
Jiang, YS., Chen, WM. Task scheduling for grid computing systems using a genetic algorithm. J Supercomput 71, 1357–1377 (2015). https://doi.org/10.1007/s11227-014-1368-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-014-1368-6