154
Views
0
CrossRef citations to date
0
Altmetric
Articles

Genetic algorithm based on-arrival task scheduling on distributed computing platform

ORCID Icon &
Pages 887-896 | Received 23 Mar 2021, Accepted 17 Aug 2021, Published online: 12 Sep 2021

References

  • Nzanywayingoma F, Yang Y. Efficient resource management techniques in cloud computing environment: a review and discussion. Int J Comput Appl. 2019;41(3):165–182.
  • Kang Q, He H, Song H. Task assignment in heterogeneous computing systems using an effective iterated greedy algorithm. J Syst Softw. 2011;84(6):985–992. DOI:10.1016/j.jss.2011.01.051
  • Topcuoglu H, Hariri S, Wu MY. Performance-effective and low-complexity task scheduling for heterogeneous computing. Parallel Distrib Syst. … . 2002;13(3):260–274. DOI:10.1109/71.993206
  • Woo SH, Yang SB, Kim SD, et al. Task scheduling in distributed computing systems with a genetic algorithm. Proc Conf High Perform Comput Inf Superhighway, HPC Asia’97. 1997: 301–305.
  • Oh J, Wu C. Genetic-algorithm-based real-time task scheduling with multiple goals. J Syst Softw. 2004;71(3):245–258. DOI:10.1016/S0164-1212(02)00147-4
  • Gen M, Yoo M. Real time tasks scheduling using hybrid genetic algorithm. Stud Comput Intell. 2008;96:319–350.
  • Omara FA, Arafa MM. Genetic algorithms for task scheduling problem. J Parallel Distrib Comput. 2010;70(1):13–22. DOI:10.1016/j.jpdc.2009.09.009
  • Choe R, Yuan H, Yang Y, et al. Real-time scheduling of twin stacking cranes in an automated container terminal using a genetic algorithm. Proc ACM Symp Appl Comput. 2012: 238–243. DOI:10.1145/2245276.2245323
  • Xu Y, Li K, Hu J, et al. A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf Sci. (Ny). 2014;270:255–287. DOI:10.1016/j.ins.2014.02.122
  • Page AJ, Keane TM, Naughton TJ. Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system. J Parallel Distrib Comput. 2010;70(7):758–766. DOI:10.1016/j.jpdc.2010.03.011
  • Akbari M, Rashidi H, Alizadeh SH. An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems. Eng Appl Artif Intell. 2017;61(February):35–46. DOI:10.1016/j.engappai.2017.02.013
  • Kundakci N, Kulak O. Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem. Comput Ind Eng 2016;96:31–51. DOI:10.1016/j.cie.2016.03.011
  • Wenxiang X, Yongwen H, Wei L, et al. A multi-objective scheduling method for distributed and flexible job shop based on hybrid genetic algorithm and tabu search considering operation outsourcing and carbon emission. Comput Ind Eng 2021;157(107318):0–0.
  • Shukla AK, Pippal SK, Chauhan SS. An empirical evaluation of teaching–learning-based optimization, genetic algorithm and particle swarm optimization. Int J Comput Appl. 2019;7074:1–15. DOI:10.1080/1206212X.2019.1686562
  • Bochenina K, Butakov N, Dukhanov A, et al. A clustering-based approach to static scheduling of multiple workflows with soft deadlines in heterogeneous distributed systems. Procedia Comput Sci. 2015;51:2827–2831. DOI:10.1016/j.procs.2015.05.442
  • Haidri RA, Katti CP, Saxena PC. Capacity based deadline aware dynamic load balancing (CPDALB) model in cloud computing environment. Int J Comput Appl. 2019;0(0):1–15.
  • Samadi Y, Zbakh M, Tadonki C. DT-MG: many-to-one matching game for tasks scheduling towards resources optimization in cloud computing. Int J Comput Appl. 2021;43(3):233–245.
  • Liu K, Jin H, Chen J, et al. A compromised-time-cost scheduling algorithm in SwinDeW-C for instance-intensive cost-constrained workflows on a cloud computing platform. Int J High Perform Comput Appl. 2010;24(4):445–456. DOI:10.1177/1094342010369114
  • Kumar M, Sharma SC. Dynamic load balancing algorithm to minimize the makespan time and utilize the resources effectively in cloud environment. Int J Comput Appl. 2020;42(1):108–117.
  • Rangsaritratsamee R, Ferrell Jr WG, Kurz MB. Dynamic rescheduling that simultaneously considers efficiency and stability. Comput Ind Eng. 2004;46(1):1–15. DOI:10.1016/j.cie.2003.09.007
  • Barbosa JG, Moreira B. Dynamic scheduling of a batch of parallel task jobs on heterogeneous clusters. Parallel Comput. 2011;37(8):428–438. DOI:10.1016/j.parco.2010.12.004
  • Kim IY, de Weck OL. Variable chromosome length genetic algorithm for progressive refinement in topology optimization. Struct Multidiscip Optim. 2005;29(6):445–456. DOI:10.1007/s00158-004-0498-5
  • Goldberg DE, Deb K. A Comparative analysis of selection schemes used in genetic algorithms, vol. 1. Urbana: Morgan Kaufmann; 1991.
  • Amalarethinam DIG, Mary GJJ. A new DAG based dynamic task scheduling algorithm (DYTAS) for multiprocessor systems. Int J Comput Appl. 2011;19(8):24–28.
  • STG. http://www.kasahara.cs.waseda.ac.jp/schedule/index.html.
  • Blickle T, Thiele L. A mathematical analysis of tournament selection. Proceeding of the 6th International Conf. on Genetic Algorithms ICGA95, 1995.
  • Haghighat AT, Nikravan M. A hybrid genetic algorithm for process scheduling in distributed operating systems considering load balancing. The IASTED Conference on Parallel and Distributed Computing and Networks PDCN, 2005.
  • Standard Performance Evaluation Corporation. https://www.spec.org/.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.