International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 7, No. 3, 8 Mar. 2015
Cover page and Table of Contents: PDF (size: 457KB)
Study of Task Scheduling in Cloud Computing Environment Using Soft Computing Algorithms
Full Text (PDF, 457KB), PP.32-38
Views: 0 Downloads: 0
Author(s)
Saurabh Bilgaiyan
1,*
Santwana Sagnika
1
Samaresh Mishra
1
Madhabananda Das
1
Index Terms
Cloud computing, Quality of services (QoS), virtualization, scheduling, swarm based algorithms, optimization
Abstract
Cloud computing is a popular computing concept that performs processing of huge volume of data using highly accessible geographically distributed resources that can be accessed by users on the basis of Pay as per Use policy. Requirements of different users may change so the amount of processing involved in such paradigm also changes. Sometimes they need huge data processing. Such highly volumetric processing results in higher computing time and cost which is not a desirable part of a good computing model. So there must be some intelligent distribution of user's work on the available resources which will result in an optimized computing environment. This paper gives a comprehensive survey on such problems and provide a detailed analysis of some best scheduling techniques from the domain of soft computing with their performance in cloud computing.
Cite This Paper
Saurabh Bilgaiyan, Santwana Sagnika, Samaresh Mishra, Madhabananda Das, "Study of Task Scheduling in Cloud Computing Environment Using Soft Computing Algorithms", International Journal of Modern Education and Computer Science (IJMECS), vol.7, no.3, pp.32-38, 2015. DOI:10.5815/ijmecs.2015.03.05
Reference
[1]Alex Comninos, "Emerging issues: cloud computing", Southern African Internet Governance Forum, Issue Papers No. 1 of 5, 2011, pp.1-7.
[2]Muhammad Baqer Mollah, Kazi Reazul Islam and Sikder Sunbeam Islam, "Next generation of computing through cloud computing technology", 25th IEEE Canadian Conference on Electrical and Computer Engineering, 2012, pp. 1-6.
[3]Yashpalsinh Jadeja and Kirit Modi, "Cloud computing - concepts, architecture and challenges", International Conference on Computing, Electronics and Electrical Technologies, 2012, pp.877-880.
[4]L. Guo, S. Zhao, S. Shen and C. Jiang, C, "Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm", Journal Of Networks, Vol. 7, No. 3, March 2012, 547-553.
[5]CT Lin, “Comparative Based Analysis of Scheduling Algorithms for Resource Management in Cloud Computing Environment”, JCSE International Journal of Computer Science and Engineering, Vol.1, No.1, 2013, 17-23.
[6]Savitha. P and J Geetha Reddy," A Review Work on Task Scheduling in Cloud Computing Using Genetic Algorithm”, International Journal of Scientific & Technology Research, Vol. 2, Issue 8, 2013, pp.241-244.
[7]Sourav Banerjee, Mainak Adhikari and Utpal Biswas, "Advanced Task Scheduling for Cloud Service Provider Using Genetic Algorithm", IOSR Journal of Engineering, Vol. 2, No. 7, 2012, pp. 141-147.
[8]Pardeep Kumar and Amandeep Verma, "Scheduling Using Improved Genetic Algorithm in Cloud Computing for Independent Tasks", International Conference on Advances in Computing, Communications and Informatics, 2012, pp.137-142.
[9]Lizheng Guo and Guojin Shao, "Multi-objective Task Assignment in Cloud Computing by Particle Swarm Optimization”, 8th International conference on Wireless Communications, Networking and Mobile Computing (WiCOM), 2012, pp.1-4.
[10]Suraj Pandey, LinlinWu, Siddeswara Mayura Guru and Rajkumar Buyya, “A Particle Swarm Optimization-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments”, IEEE International Conference on Advanced Information Networking and Applications, 2010, pp. 400 – 407.
[11]Sheng-Jun Xue and Wu, “Scheduling Workflow in Cloud Computing Based on Hybrid Particle Swarm Algorithm", TELKOMNIKA, Vol.10, No.7, 2012, pp. 1560-1566.
[12]C.W. Chiang, Y.C. Lee, C.N. Lee and T.Y Chou, “Ant colony optimisation for task matching and scheduling", IEEE Proc.-Computer Digital. Techniques, Vol. 153, No. 6, 2006, pp.373-379.
[13]Linan Zhu, Qingshui Li and Lingna He, "Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm", IJCSI International Journal of Computer Science Issues, Vol. 9, No. 2, 2012, pp.54-58.
[14]Hui Liu, Dong Xu and HuaiKou Miao, "Ant Colony Optimization Based Service flow Scheduling with Various QoS Requirements in Cloud Computing", First ACIS International Symposium on Software and Network Engineering, 2011 , pp.53-57.
[15]Dhinesh Babu L.D. and P. Venkata Krishna, "Honey bee behaviour inspired load balancing of tasks in cloud computing environments", Applied Soft Computing, Vol. 13, No. 5, 2013, pp. 2292–2303.
[16]Sung-Soo Kim, Ji-Hwan Byeon, Hongbo Liu, Ajith Abraham and Seán McLoone, "Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization", Soft Computing, Vol. 17, No. 5, 2013, pp 867-882.
[17]Saurabh Bilgaiyan, Santwana Sagnika and Madhabananda Das, "Workflow Scheduling in Cloud Computing Environment Using Cat Swarm Optimization", International Journal of Computer Applications, Vol .89, No.2, 2014, pp. 680 - 685.
[18]Andrew J. Page and Thomas J. Naughton, "Dynamic task scheduling using genetic algorithms for heterogeneous
distributed computing", 19th IEEE International Conference on Parallel and Distributed Processing Symposium, 2005, pp. 189a.
[19]J. H. Holland. Adaptation in Natural and Artificial Systems.MIT Press, Cambridge, MA, USA, 1992.
[20]W. Qing, and Z. Han-Chao, "Optimization of Task Allocation And Knowledge Workers Scheduling Based-on Particle Swarm Optimization, "In Proceedings of IEEE International Conference on Electric Information and Control Engineering, 2011, pp. 574-578.
[21]Liang Bai, Yan-Li Hu, Song-Yang Lao, Wei-Ming Zhang, "Task scheduling with load balancing using multiple ant colonies optimization in grid computing", IEEE Sixth International Conference on NaturalComputation (ICNC 2010), 2010, pp.2715-2719.
[22]Salim Bitam, "Bees Life Algorithm for Job Scheduling in Cloud Computing", Proceedings of The Third International Conference on Communications and Information Technology, 2012, pp. 186-191.
[23]Chenhong Zhao, Shanshan Zhang and Qingfeng Liu, "Independent Tasks Scheduling Based on Genetic Algorithm in Cloud Computing ", 5th IEEE International Conference on Wireless Communications, Networking and Mobile Computing, 2009, pp. 1-4.
[24]S. Ravichandran and Dr. E.R. Naganathan, "Dynamic Scheduling of Data Using Genetic Algorithm in Cloud Computing", International Journal of Computing Algorithm, Vol. 02, Issue 01, 2013, pp. 127-133.
[25]Zhangjun Wu, Zhiwei Ni, Lichuan Gu and Xiao Liu , "A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling", IEEE International Conference Computational Intelligence and Security (CIS), 2010, pp.184-188.
[26]Kun Li, Gaochao Xu, Guangyu Zhao, Yushuang Dong and D. Wang, "Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization", Sixth Annual IEEE Chinagrid Conference (ChinaGrid), 2011, pp. 3-9.