Abstract

Cloud computing is basically internet based computing while software, information and shared resources are provided to devices and computers on demand, like electricity grid. With the fusion of network technology and traditional computing technology such as distributed computing parallel computing, grid computing a cloud computing product is formed. Task scheduling is the major concern in the field of cloud computing. As the use of cloud computing increases, the burden on the cloud network also increases. So, it's the duty of the scheduler to make cloud efficient to solve client's tasks. This work focuses on the same to achieve the objective of optimized task scheduling where improved genetic algorithm is proposed. Genetic algorithm is artificial intelligent based soft computing technique to optimize the process. Here in this work, genetic algorithm is enhanced using new fitness function based on mean and grand mean values. This optimization can be implemented on both ends, for job scheduling and resource scheduling. This will schedule the whole process and optimize as much as possible. The results analysis also proves the cloud system's increased efficiency for task scheduling.

References

• Federico Etro, "Introducing Cloud Computing", London Conference on Cloud Computing For the Public Sector, November 4, 2010, pp. 01-20.
• Ling Qian et al, "Cloud Computing: An Overview", 1st International Conference, December, 2009, pp. 626-63.
• Dimpi Rani, Rajiv Kumar Ranjan, "A Comparative Study of SaaS, PaaS and IaaS in Cloud Computing", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 6, June 2014, pp. 158-161.
• Ram Kumar Sharma and Nagesh Sharma, "A Dynamic Optimization Algorithm for Task Scheduling in Cloud Computing With Resource Utilization", International Journal of Scientific Engineering and Technology, Volume No. 2, Issue No. 10, pp. 1062-1068.
• Lu Huang, Hai-shan Chen and Ting-ting Hu, "Survey on Resource Allocation Policy and Job Scheduling Algorithms of Cloud Computing", Journal of Software, Vol. 8, No. 2, February 2013, pp. 480-487.
• Shu-Ching, Wang Kuo-Qin, Yan, "A Three-Phases Scheduling in a Hierarchical Cloud Computing Network", 2011 Third International Conference on Communications and Mobile Computing, 978-0-7695-4357-4/11, 2011 IEEE DOI 10. 1109/CMC. 2011. 28
• Hitech A. Ravani et al, "Genetic Algorithm Based Resource Scheduling Technique in Cloud Computing," Volume 1, Issue 7, December 2013.
• Nimisha Singla et al, "Review of Efficient Resource Scheduling Algorithms in Cloud Computing", Volume 3, Issue 8, August 2013.
• Sung Ho Jang, Tae Young Kim, Jae Kwon Kim and Jong Sik Lee, " The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing," International Journal of Control and Automation,Vol. 5, No. 4, December, 2012.
• Tarun Goyal & Aakanksha Agrawal, "Host Scheduling Algorithm Using Genetic Algorithm In Cloud Computing Environment," International Journal of Research in Engineering & Technology (IJRET) Vol. 1, Issue 1, June 2013.