Abstract
Scheduling plays a vital role in our real life, same as CPU scheduling majorly affects the performance of computer system. For better performance, scheduling depends upon the parameters of jobs (arrival time, burst time, priority, etc.). Different algorithms have been used to find the above factors. Many algorithms such as FCFS, SJF, round-robin, priority are applied, but all these techniques provide a sequence of jobs relevant to their properties. Developing an appropriate sequence using previously known algorithms takes exponential time. This paper proposes an efficient method for process scheduling using a deadline-aware approximation algorithm, where required schedule has a certain weightage of priority and burst time of job. Here, GA and modified GA are compared in terms of number of iterations, number of test cases, requirement percentage and tardiness (fitness value). The results demonstrate that modified GA approach produces solutions very close to the optimal one in comparison with GA.
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Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
De Jong, K.A.: Adaptive system design: a genetic approach. IEEE Trans. Syst. Man Cybern. 10, 566–574 (1980)
Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. Future Gener. Comput. Syst. 26, 608–621 (2010)
Goldberg, D.E.: Genetic Algorithms in Search. Optimization and Machine Learning. Addison Wesley, Boston (1989)
Yao, W., et al.: Genetic Scheduling on Minimal Processing Elements in the Grid. Springer, Berlin (2002)
Fayad, C., Petrovic, S.: A Genetic Algorithm for the Real-World Fuzzy Job Shop Scheduling. Innovations in Applied Artificial Intelligence, pp. 524–533. Springer, Berlin (2005)
Biegel, J.E., Davern, J.J.: Genetic algorithms and job-shop scheduling. Comput. Ind. Eng. 19, 81–91 (1990)
Fogel, D.B.: Evolutionary algorithms in theory and practice. Complexity 2(4), 26–27 (1997)
Li, H., Wang, L., Liu, J.: Task scheduling of computational grid based on particle swarm algorithm. In: Third International Joint Conference on Computational Science and Optimization (CSO), vol. 2, pp. 332–336, May 2010
Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: Chinagrid Conference (ChinaGrid), 2011 Sixth Annual, pp. 3–9, Aug 2011
Houshmand, M., Soleymanpour, E., Salami, H., Amerian, M., Deldari, H.: Efficient scheduling of task graphs to multiprocessors using a combination of modified simulated annealing and list-based scheduling. In: Third International Symposium on Intelligent Information Technology and Security Informatics (IITSI), pp. 350–354, April 2010
Shanmugapriya, R., Padmavathi, S., Shalinie, S.: Contention awareness in task scheduling using tabu search. In: IEEE International Advance Computing Conference, IACC 2009, pp. 272–277, March 2009
Wong, Y.W., Goh, R., Kuo, S.-H., Low, M.: A tabu search for the heterogeneous dag scheduling problem. In 15th International Conference on Parallel and Distributed Systems (ICPADS), pp. 663–670, Dec 2009
Gaba, V., Parashar, A: Comparison of processor scheduling algorithms using genetic approach. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(8), 37–45 (2012)
Xu, Y., Li, K., Khac, T.T., Qiu, M.: A multiple priority queueing genetic algorithm for task scheduling on heterogeneous computing systems. In: 2012 IEEE 14th International Conference on High Performance Computing and Communications
Neshat, M., Sargolzaei, M., Najaran, A., Adeli, A.: The new method of adaptive CPU scheduling using Fonseca and Fleming’s genetic algorithm. J. Theor. Appl. Inform. Technol. 37(1) (2012)
Patel, J., Solanki, A.K.: Performance enhancement of CPU scheduling by hybrid algorithms using genetic approach. Int. J. Adv. Res. Comput. Eng. Technol. 1(4) (2012)
Moin, N.H., Chung Sin, O., Omar, M.: Hybrid genetic algorithm with multiparents crossover for job shop scheduling problems. Math. Probl. Eng. 2015(210680), 12 (2015)
Umbarkar, A.J.: Dual population genetic algorithm for solving constrained optimization problems. I.J. Intell. Syst. Appl. 02, 34–40 (2015)
Yan-Fang, Y., Yue, Y.: An improved genetic algorithm to the job shop scheduling problem. J. Chem. Pharm. Res. 7(4), 322–325 (2015)
Butt, M.A., Akram, M.: A novel fuzzy decision-making system for CPU scheduling algorithm. Neural Comput. Appl. doi:10.1007/s 00521-015-1987-8
Bhadula, S.J., Rohilla, B., Nautiyal, B.: A genetic algorithm for scheduling jobs with burst time and priorities. IOSR J. Comput. Eng. 17(4), 69–74 (2015). IOSR-JCE. e-ISSN: 2278-0661, p-ISSN: 2278-8727
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Pal, H., Rohilla, B., Singh, T. (2018). A Deadline-Aware Modified Genetic Algorithm for Scheduling Jobs with Burst Time and Priorities. In: Panigrahi, B., Hoda, M., Sharma, V., Goel, S. (eds) Nature Inspired Computing. Advances in Intelligent Systems and Computing, vol 652. Springer, Singapore. https://doi.org/10.1007/978-981-10-6747-1_7
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DOI: https://doi.org/10.1007/978-981-10-6747-1_7
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