A Review on Task Scheduling Approaches Based on Weighted Round Robin Algorithm in Cloud Environment
Keywords:
cloud computing, scheduling, load balancing, virtual machineAbstract
Cloud computing uses the concepts of scheduling and load balancing to move around tasks to underutilized VMs for effectively sharing the resources. The scheduling of the tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple tasks and they may execute the tasks in multiple VMs or in the same VM’s multiple cores. Also, the jobs arrive during the run time of the server in changeable random intervals under various load conditions. The participating various resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capability of each virtual machine (VM), the task length of each requested job, and the of multiple tasks. Performance of the proposed algorithm is studied by comparing with the accessible methods.
References
[1] Z. Xiao, W. Song, and Q. Chen, “Dynamic resource allocation using virtual machines for cloud computing environment,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1107–1117, 2013. View at Publisher • View at Google Scholar • View at Scopus
[2] L. D. Dhinesh Babu and P. Venkata Krishna, “Honey bee behavior inspired load balancing of tasks in cloud computing environments,” Applied Soft Computing Journal, vol. 13, no. 5, pp. 2292–2303, 2013.View at Publisher • View at Google Scholar • View at Scopus
[3] J. Cao, K. Li, and I. Stojmenovic, “Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers,” IEEE Transactions on Computers, vol. 63, no. 1, pp. 45–58, 2014. View at Publisher • View at Google Scholar • View at MathSciNet • View at Scopus
[4] R. N. Calheiros and R. Buyya, “Meeting deadlines of scientific workflows in public clouds with tasks replication,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 7, pp. 1787–1796, 2014. View at Publisher • View at Google Scholar • View at Scopus
[5] R. Basker, V. Rhymend Uthariaraj, and D. Chitra Devi, “An enhanced scheduling in weighted round robin for the cloud infrastructure services,” International Journal of Recent Advance in Engineering & Technology, vol. 2, no. 3, pp. 81–86, 2014. View at Google Scholar
[6] Z. Yu, F. Menng, and H. Chen, “An efficient list scheduling algorithm of dependent task in grid,” in Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT `10), IEEE, Chengdu, China, July 2010.
[7] H. M. Fard and H. Deldari, “An economic approach for scheduling dependent tasks in grid computing,” in Proceedings of the 11th IEEE International Conference on Computational Science and Engineering (CSEWorkshops `08), pp. 71–76, IEEE, San Paulo, Brazil, July 2008. View at Publisher • View at Google Scholar • View at Scopus
[8] W. Kadri, B. Yagoubi, and M. Meddeber, “Efficient dependent tasks assignment algorithm for grid computing environment,” in Proceedings of the 2nd International Symposium on Modelling and Implementation of Complex Systems (MISC `12), Constantine, Algeria, May 2012.
[9] Y. Xu, K. Li, L. He, and T. K. Truong, “A DAG scheduling scheme on heterogeneous computing systems using double molecular structure-based chemical reaction optimization,” Journal of Parallel and Distributed Computing, vol. 73, no. 9, pp. 1306–1322, 2013. View at Publisher • View at Google Scholar • View at Scopus
[10] B. Xu, C. Zhao, E. Hu, and B. Hu, “Job scheduling algorithm based on Berger model in cloud environment,” Advances in Engineering Software, vol. 42, no. 7, pp. 419–425, 2011. View at Publisher• View at Google Scholar • View at Scopus
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