Improved Scheduling Algorithm in Cloud Computing

Authors

  • Varinder Saggar  M. Tech (Scholar), CSE Depatment Desh Bhagat University, Mandi Gobindgarh, Punjab, India
  • Manoj Kumar Srivastava  CSE Department, Desh Bhagat University, Mandi Gobindgarh, Punjab, India

Keywords:

Improved Scheduling algorithm, Cloud, Job Scheduling in Parallel, Batch Workloads, Makespan.

Abstract

The current era of an emerging technology is cloud computing. It is internet based computing, works as pay-per-use model and process large data. The cloud Service provider goal is to manage resources efficiently, So, in cloud computing the mechanism of Scheduling has an important function. The revised scheduling technique is meant to improve the server performance and decrease the switching time to increase the use of resources. Different sorts of scheduling algorithms have been studied and analysed in this research to deliver efficient cloud services. The improved Scheduling algorithm prioritises the task, which improves computer performance and does my best possible efforts to limit the duration and duration of waiting. A CloudSim tool is used to simulate the suggested approach.

References

  1. K. Liu; Y. Yang; J. Chen, X. Liu; D. Yuan; H. Jin, “A Compromised-Time- Cost Scheduling Algorithm in SwinDeW-C for Instance-intensive Cost- Constrained Workflows on Cloud Computing Plat- form”, International Journal of High Performance Computing Applications, vol.24, May,2010, Page no.4 445 456.
  2. Suraj Pandey1; LinlinWu1; Siddeswara Mayura Guru; Rajkumar Buyya, “A Particle Swarm Opti- mization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments” 24th IEEE International Conference onAdvanced Information Networking and Applications, 2010.
  3. Cui Lin, Shiyong Lu, “Scheduling ScientificWorkflows Elasticallyfor Cloud Comput- ing” inIEEE 4th International Conferenceon Cloud Computing, 2011.
  4. Salim Bitam, “Bees LifeAlgorithm for Job Sched- uling in Cloud Computing,” in second international conferenceoncommunicationandinformationtech- nology, Feb 2012.
  5. Abirami S.P and Shalini Ramanathan, “ Linear Scheduling Strategy for Resource Allocation in Cloud Environment”, InternationalJournalon Cloud Computing: Services and Architecture(IJCCSA),Vol.2, No.1,February 2012.
  6. Wei Wang, Guosun Zeng, Daizhong Tang, Jing Yao, “Cloud-DLS: Dynamictrusted scheduling for Cloud computing”, SciVerse ScienceDirect , Ex- pert Systems withApplications 39, 2012.
  7. R. Santhosh, T. Ravichandran, “Pre-emptive Scheduling ofOn-line RealTime ServiceswithTask Migration for Cloud Computing”, International Conference on Pattern Recognition, IEEE, Feburary 2013.
  8. El-Sayed T. El-kenawy, Ali Ibraheem El-Desoky, Mohamed F. Al-rahamawy “Extended Max-Min Scheduling Using Petri Net and Load Balancing” International Journal of Soft Computing and Engi- neering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-4, September 2012.
  9. Xiaomin Zhua, Chuan Hea, Kenli Li, Xiao Qin, “Adaptive energy-efficient scheduling for realtime tasks on DVS-enabled heterogeneous clusters”, J.Parallel Distrib. Comput, SciVerse ScienceDirect, 2012, Elsevier Inc.
  10. Anish Das Sarma, Christopher Olston,Xiaodan Wang, Randal Burns : CoScan: Cooperative Scan Sharing in the Cloud.

Downloads

Published

2021-07-30

Issue

Section

Research Articles

How to Cite

[1]
Varinder Saggar, Manoj Kumar Srivastava, " Improved Scheduling Algorithm in Cloud Computing, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 4, pp.156-161, July-August-2021.