183
Views
24
CrossRef citations to date
0
Altmetric
Articles

MFGMTS: Epsilon Constraint-Based Modified Fractional Grey Wolf Optimizer for Multi-Objective Task Scheduling in Cloud Computing

&

REFERENCES

  • H. He , G. Xu , S. Pang , and Z. Zhao , “AMTS: Adaptive multi-objective task scheduling strategy in cloud computing,” China Commun., Vol. 13, no. 4, pp. 162–71, Apr. 2016.
  • X. Lin , Y. Wang , Q. Xie , and M. Pedram , “Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment,” IEEE Trans. Serv. Comput., Vol. 8, no. 2, pp. 175–86, Apr. 2015.
  • M. Abdullahi , M. A. Ngadi , and S. M. Abdulhamid , “Symbiotic organism search optimization based task scheduling in cloud computing environment,” Future Gener. Comput. Syst., Vol. 56, pp. 640–50, Mar. 2016.
  • M. Masdari , S. V. Kardan , Z. Shahi , and S. I. Azar , “Towards workflow scheduling in cloud computing: A comprehensive analysis,” J. Netw. Comput. Appl., Vol. 66, pp. 64–82, May 2016.
  • M. Feng , X. Wang , Y. Zhang , and J. Li , “Multi-objective particle swarm optimization for resource allocation in cloud computing,” in Proceedings of IEEE 2nd International Conference on Cloud Computing and Intelligence Systems , Hangzhou, China, 30 Oct.–1 Nov. 2012.
  • F. Zhanga , J. Caob , K. Lic , S. U. Khand , and K. Hwange , “Multi-objective scheduling of many tasks in cloud platforms,” Future Gener. Comput. Syst., Vol. 37, pp. 309–20, Jul. 2014.
  • S. H. H. Madni , M. S. A. Latiff , Y. Coulibaly , and S. M. Abdulhamid , “Resource scheduling for infrastructure as a service (IaaS) in cloud computing: Challenges and opportunities,” J. Netw. Comput. Appl., Vol. 68, pp. 173–200, Jun. 2016.
  • T. Shi , M. Yang , X. Li , Q. Lei , and Y. Jiang , “An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds,” Pervas. Mobile Comput., Vol. 27, pp. 90–105, Apr. 2016.
  • W. Kong , Y. Lei , and J. Ma , “Virtual machine resource scheduling algorithm for cloud computing based on auction mechanism,” Optik – Int. J. Light Electron Optics, Vol. 127, no. 12, pp. 5099–104, Jun. 2016.
  • L. Zeng , B. Veeravalli , and A. Y. Zomaya , “An integrated task computation and data management scheduling strategy for workflow applications in cloud environments,” J. Netw. Comput. Appl., Vol. 50, pp. 39–48, Apr. 2015.
  • S. K. Panda , I. Gupta , and P. K. Jana , “Allocation-aware task scheduling for heterogeneous multi-cloud systems,” Proc. Comput. Sci., Vol. 50, pp. 176–84, Mar. 2015.
  • S. Chander , P. Vijaya , and P. Dhyani , “Fractional lion algorithm-an optimization algorithm for data clustering,” J. Comput. Sci., Vol. 12, no. 7, pp. 323–40, Aug. 2016.
  • S. S. Pashaki , E. Teymourian , V. Kayvanfar , G. H. M. Komaki , and A. Sajadi , “Group technology-based model and cuckoo optimization algorithm for resource allocation in cloud computing,” IFAC-PapersOnLine, Vol. 48, no. 3, pp. 1140–5, May 2015.
  • S. Chander , P. Vijaya , and P. Dhyani , “DOFL: Kernel based directive operative fractional lion optimisation algorithm for data clustering,” Int. Rev. Comput. Soft., Vol. 11, no. 8, pp. 701, Aug. 2016.
  • S. Su , J. Li , Q. Huang , X. Huang , K. Shuang , and J. Wang , “Cost-efficient task scheduling for executing large programs in the cloud,” Parallel Comput., Vol. 39, no. 4–5, pp. 177–88, Apr.–May 2013.
  • J. Tsai , J. Fang , and J. Chou , “Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm,” Comput. Oper. Res., Vol. 40, no. 12, pp. 3045–55, Dec. 2013.
  • Z. Zhu , G. Zhang , M. Li , and X. Liu , “Evolutionary multi-objective workflow scheduling in cloud,” IEEE Trans. Parallel Distrib. Syst., Vol. 27, no. 5, pp. 1344–57, May 2016.
  • M. A. Rodriguez , and R. Buyya , “Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds,” IEEE Trans. Cloud Comput., Vol. 2, no. 2, pp. 222–35, Jun. 2014.
  • S. Kim , J. Byeon , H. Yu , and H. Liu , “Biogeography-based optimization for optimal job scheduling in cloud computing,” Appl. Math. Comput., Vol. 247, pp. 266–80, Nov. 2014.
  • M. Vasile , F. Pop , R. Tutueanu , V. Cristea , and J. Kolodziej , “Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing,” Future Gener. Comput. Syst., Vol. 51, pp. 61–71, Oct. 2015.
  • L. Zuo , L. Shu , S. Dong , C. Zhu , and T. Hara , “A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing,” IEEE Access, Vol. 3, pp. 2687–99, Dec. 2015.
  • X. Xu , L. Cao , X. Wang , X. Xu , L. Cao , and X. Wang , “Resource pre-allocation algorithms for low-energy task scheduling of cloud computing,” J. Syst. Eng. Electron, Vol. 27, no. 2, pp. 457–69, Apr. 2016.
  • T. Wen , Z. Zhang , and M. Wang , “A parallel bee colony algorithm for resource allocation application in cloud computing environment,” in Proceedings of IEEE International Conference on Data Science and Data Intensive System s, Sydney, NSW, 11–13, Dec. 2015, pp. 153–60.
  • A. V. Lakra , and D. K. Yadav , “Multi-objective tasks scheduling algorithm for cloud computing throughput optimization,” Proc. Comput. Sci., Vol. 48, pp. 107–13, Dec. 2015.
  • R. K. Jena , “Multi objective task scheduling in cloud environment using nested PSO framework,” Proc. Comput. Sci., Vol. 57, pp. 1219–27, Dec. 2015.
  • S. Mirjalili , S. M. Mirjalili , and A. Lewis , “Grey wolf optimizer,” Adv. Eng. Softw., Vol. 69, pp. 46–61, Mar. 2014.
  • E. Daniel , J. Anitha , and J. Gnanaraj , “Optimum Laplacian wavelet mask based medical image using hybrid cuckoo search – grey wolf optimization algorithm,” Knowl.-Based Syst., Vol. 131, pp. 58–69, Sep. 2017.
  • E. Daniel , J. Anitha , K. K. Kamaleshwaran , and I. Rani , “Optimum spectrum mask based medical image fusion using gray wolf optimization,” Biomed. Signal Process., Vol. 34, no. 30, pp. 36–43, Apr. 2017.
  • E. J. S. Pires , J. A. T. Machado , P. B. M. Oliveira , J. B. Cunha , and L. Mendes , “Particle swarm optimization with fractional-order velocity,” Nonlinear Dyn., Vol. 61, no. 1–2, pp. 295–301, Jul. 2010.

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.