A QoS Aware FTOPSIS-WOA based task scheduling algorithm with load balancing technique for the cloud computing environment

Jitendra Kumar Samriya; Narander Kumar

doi:10.17485/IJST/v13i35.1314

Article

A QoS Aware FTOPSIS-WOA based task scheduling algorithm with load balancing technique for the cloud computing environment

VIEWS 1428
PDF 717

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i35.1314

Year: 2020, Volume: 13, Issue: 35, Pages: 3675-3684

Original Article

A QoS Aware FTOPSIS-WOA based task scheduling algorithm with load balancing technique for the cloud computing environment

Jitendra Kumar Samriya¹, Narander Kumar^2*

¹Research Scholar, Department of Computer Science, Babasaheb Bhimrao Ambedkar University (A Central University), Lucknow, 226025, India
²Assistant Professor, Department of Computer Science, Babasaheb Bhimrao Ambedkar University (A Central University), Lucknow, 226025, India

*Corresponding Author
Email: [email protected]

Received Date:08 August 2020, Accepted Date:13 September 2020, Published Date:05 October 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To perform task scheduling with minimising the makespan through implementing an effective load balancing approach. Methods: In this study, the Fuzzy Topsis algorithm (FTPOSIS) is used for the task scheduling and the makespan is minimised with the effective load balancing by modelling the whale optimization algorithm (WOA). Findings: This proposed model controls the admittance of the requests by achieving target QoS in terms of response time. Hence, the admittance is controlled so that the requests which are accepted do not face a delay greater than the time limit stated in the SLA. Using CloudSim tool the simulation is done and the results are exhibited. The effectiveness of the intended algorithm is compared with the existing methods.Novelty: The novelty of this study includes increasing the throughput of the cloud system by reducing the makespan of the cloud scheduling process.Reducing SLA violations and improving the QoS can efficiently give assurance to reduce the delay of transmission, packet loss rate of data. Attaining a balance between constrained resources and QoS.

Keywords: Cloud computing system; load balancing; scheduling; makespan;FTOPSIS; WOA; task scheduling

References

Ma J, Li W, Fu T, Yan L, Hu G. A novel dynamic task scheduling algorithm based on improved genetic algorithm in cloud computing. Wireless Communications, Networking and Applications. 2016;p. 829–835.
Jana B, Chakraborty M, Mandal T. A task scheduling technique based on particle swarm optimization algorithm in cloud environment. In: Soft Computing: Theories and Applications. (pp. 525-536) Springer. 2019.
Reddy G, Narendrababu SP, Kumar. Multi objective task scheduling algorithm for cloud computing using whale optimization technique. In: International Conference on Next Generation Computing Technologies. (pp. 286-297) Springer. 2017.
Saha S, Pal S, Pattnaik PK. A novel scheduling algorithm for cloud computing environment. Computational Intelligence in Data Mining. 2016;1:387–398.
Moon Y, Yu H, Gil JM, Lim J. A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments. Human-centric Computing and Information Sciences. 2017;7(1). Available from: https://dx.doi.org/10.1186/s13673-017-0109-2
Panda KS, Jana KP. An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems. Cluster Computing. 2019;22(2):509–527. Available from: https://dx.doi.org/10.1007/s10586-018-2858-8
Kumar AMS, Venkatesan M. Task scheduling in a cloud computing environment using HGPSO algorithm. Cluster Computing. 2019;22(S1):2179–2185. Available from: https://dx.doi.org/10.1007/s10586-018-2515-2
Aziza H, Krichen S. Bi-objective decision support system for task-scheduling based on genetic algorithm in cloud computing. Computing. 2018;100(2):65–91. Available from: https://dx.doi.org/10.1007/s00607-017-0566-5
Gabi D, Ismail AS, Zainal A, Zakaria Z. Quality of service (QoS) task scheduling algorithm with Taguchi orthogonal approach for cloud computing environment. In: International Conference of Reliable Information and Communication Technology. (pp. 641-649) Springer. 2017.
Agarwal M, Srivastava GMS. A cuckoo search algorithm-based task scheduling in cloud computing. Advances in Computer and Computational Sciences. 2018;p. 293–299.
Potluri S, Rao KS. Simulation of QoS-Based Task Scheduling Policy for Dependent and Independent Tasks in a Cloud Environment. Smart Intelligent Computing and Applications. ;p. 515–525.
Arulkumar V, Bhalaji N. Performance analysis of nature inspired load balancing algorithm in cloud environment. Journal of Ambient Intelligence and Humanized Computing. 2020;2020:1–8. Available from: https://dx.doi.org/10.1007/s12652-019-01655-x
Gupta A, Bhadauria HS, Singh A. Load balancing based hyper heuristic algorithm for cloud task scheduling. Journal of Ambient Intelligence and Humanized Computing. 2020;2020:1–8. Available from: https://dx.doi.org/10.1007/s12652-020-02127-3
Arul Xavier VM, Annadurai S. Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Cluster Computing. 2019;22(S1):287–297. Available from: https://dx.doi.org/10.1007/s10586-018-1823-x
Panwar N, Negi S, Rauthan MMS, Vaisla KS. TOPSIS–PSO inspired non-preemptive tasks scheduling algorithm in cloud environment. Cluster Computing. 2019;22(4):1379–1396. Available from: https://dx.doi.org/10.1007/s10586-019-02915-3
Abualigah L, Diabat A. A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Computing. 2020;2020:1–19. Available from: https://dx.doi.org/10.1007/s10586-020-03075-5
Alazzam H, Alhenawi E, Al-Sayyed R. A hybrid job scheduling algorithm based on Tabu and Harmony search algorithms. The Journal of Supercomputing. 2019;75(12):7994–8011. Available from: https://dx.doi.org/10.1007/s11227-019-02936-0
Valarmathi R, Sheela T. Ranging and tuning based particle swarm optimization with bat algorithm for task scheduling in cloud computing. Cluster Computing. 2019;22(5):11975–11988.
Farid M, Latip R, Hussin M, Hamid NAWA. A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing. Symmetry. 2020;12(4):551. Available from: https://dx.doi.org/10.3390/sym12040551
Al-Rahayfeh A, Atiewi S, Abuhussein A, Almiani M. Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms. Future Internet. 2019;11(5):109. Available from: https://dx.doi.org/10.3390/fi11050109
Ma X, Gao H, Xu H, Bian M. An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing. EURASIP Journal on Wireless Communications and Networking. 2019;(1). Available from: https://doi.org/10.1186/s13638-019-1557-3
Boutkhoum O, Hanine M, Agouti T, Tikniouine A. A decision-making approach based on fuzzy AHP-TOPSIS methodology for selecting the appropriate cloud solution to manage big data projects. International Journal of System Assurance Engineering and Management. 2017;8(S2):1237–1253. Available from: https://dx.doi.org/10.1007/s13198-017-0592-x

Copyright

© 2020 Samriya & Kumar.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).