Cloud computing has become an important phenomena in computing and internet era. Cloud computing has enabled service providers to completely present their services in cloud platform. The main challenge is to fully utilize those resources in such a way so that system performance has increased and energy utilization has decreased. In this paper, we presented a combined strategy that allows more than two users to schedule the task. Experimentation shows that our proposed strategy increases the success rate by significantly decreasing the energy consumption and increases the cloud processor performance. The purposed criteria are shown by comparing it with traditional algorithm.

Cloud Computing, Job Scheduling in Cloud for performance improvement, combined strategy

[1]Yintian, Wang, RuanaRao,”A Round Robin with multiple feedback job scheduler in hadoop”,IEEE International Conference, Shangai Ziotang University, 2014 China.
[2] Xiuhua Li,Chunsheng Zhu, “ Job scheduling for cloud computing integrated with wireless network” ,IEEE 6th International conference, 2014, Vancouver, Canada.
[3] Alaka Ananth ,”Game theoretic strategies for scheduling the jobs,” IEEE 5th International Conference, Suratkal,2014 India.
[4]Cheng Dazhao, "Resource And Deadline-Aware Job Scheduling In Dynamic Hadoop Clusters" IEEE International Parallel and Distributed Processing Symposium (IPDPS),2015.
[5]Abhishek Gupta, "A Theoretical Comparison Of Job Scheduling Algorithms In Cloud Computing Environment, "IEEE International Conference on Next Generation Computing Technologies (NGCT),2015.
[6] Rajveer Kaur and Supriya Kinger, “Analysis of Job Scheduling Algorithms in Cloud Computing”, International Journal of Computer Trends and Technology (IJCTT), Vol. 9, Issue 7,2015 .
[7] Aparnaa, S. K., and K. Kousalya, "An Enhanced Adaptive Scoring Job Scheduling Algorithm For Minimizing Job Failure In Heterogeneous Grid Network". IEEE International Conference on Recent Trends in Information Technology (ICRTIT),2014.
[8]R. Rao, and Y. Wang, “A Round Robin With Multiple Feedback Job Scheduler In Hadoop” IEEE International Conference on Progress in Informatics and Computing, pp. 471–475,2014.
[9] Vaishali Chahar, “A Review of Multilevel Queue and Multilevel Feedback Queue Scheduling Techniques” IEEE International Journal of Advanced Research in Computer Science and Software Engineering, 2013.
[10]Chen ,Huangning, and Wenzhong Guo, "Real-Time Task Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization." in Cloud Computing and Big Data, Springer International Publishing, pp. 141-152.
[11] Mishra, Manoj Kumar, Prithviraj Mohanty, and G. B. Mund, "A Modified Grouping-Based Job Scheduling in Computational Grid" IEEE International Conference (NUiCONE) ,2011
[12] Tang Wei, "Adaptive metric-aware job scheduling for production of supercomputers", Workshops In parallel processing system (ICPPW), 41st IEEE International Conference,2012 .
[13] Zeng Chengkuan, Jiafu Tang, and Huabo Zhu, "Two Heuristic Algorithms of Job Scheduling Problem with Inter Cell Production Mode in Hybrid Operations of Machining" IEEE Control and Decision Conference (CCDC), 2013.