Task Scheduling Algorithm Based on Bacterial Foraging Optimization (BFO) in Cloud Computing
Author : Anupama Gupta, Kulveer Kaur and Rajvir KaurVolume 7 No.1 January-June 2018 pp 16-19
Abstract
Cloud computing is the architecture in which cloudlets are executed by the virtual machines. The most applicable virtual machines are selected on the basis of execution time and failure rate. Due to virtual machine overloading, the execution time and energy consumption is increased at steady rate. In this paper, BFO technique is applied in which weight of each virtual machine is calculated and the virtual machine which has the maximum weight is selected on which cloudlet will be migrated. The performance of proposed algorithm is tested by implementing it in CloudSim and analyzing it in terms of execution time, energy consumption.
Keywords
VM migration, Cloudlet, virtual machines, CloudSim
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References
[1] Guiyi Wei, Athanasios V., Vasilakos, Yao Zheng, Naixue Xiong, “A game-theoretic method of fair resource allocation for cloud computing services”, J Supercomput, Vol. 54, pp. 252–269, 2010
[2] Young Choon Lee, Albert Y. Zomaya, “Energy efficient utilization of resources in cloud computing systems”, J Supercomput, Vol. 60, pp. 268–280, 2012.
[3] Doulamis ND, Kokkinos P, Varvarigos E, “Resource selection for tasks with time requirements using spectral clustering”, IEEE Trans Comput, Vol. 63, No. 2, pp. 461–474, 2014.
[4] Abdul Hameed, Alireza Khoshkbarforoushha, Rajiv Ranjan, Prem Prakash Jayaraman, Joanna Kolodziej, Pavan Balaji, Sherali Zeadally, Qutaibah Marwan Malluhi, Nikos Tziritas, Abhinav Vishnu, Samee U. Khan, Albert Zomaya, “A survey and taxonomy on energy
efficient resource allocation techniques for cloud computing systems”, Computing, pp. 1–24, 2014.
[5] Huangke Chen, Xiaomin Zhu, Hui Guo, Jianghan Zhu, Xiao Qin, Jianhong Wu, “Towards Energy-Efficient Scheduling for Real-Time Tasks under Uncertain Cloud Computing Environment”, J Syst Softw, Vol. 99, pp. 20–35, 2015
[6] Zhanjie Wang, Xianxian Su, “Dynamically hierarchical resource-allocation algorithm in cloud computing environment”, J Supercomput, 2015.
[7] Hamid Roomi Talkhaby, Reza Parsamehr, “Cloud Computing Authentication Using Biometric-Kerberos scheme based on Strong DiffiHellman-DSA Key Exchange”, International Conference on Control, Instrumentation, Communication and Computational Technologies, vol.3, pp.104-110, 2016.
[8] Krutika K. Shah, Vahida U. Vadiya, Rutvij H. Jhaveri, “A Survey Paper on Security in Cloud Computing: A Bibliographic Analysis”, Circulation in Computer Science, vol.1, pp. 9-23, 2016.
[9] Dr. S. S. Manikandasaran, “Security Attacks and Cryptography Solutions for Data Stored in Public Cloud Storage”, International Journal of Computer Science and Information Technology & Security, vol.6, pp. 498-503, 2016.
[10] Guodong Zhu, Yue Yin, Ruoyan Cai, Kang Li, “Detecting Virtualization Specific Vulnerabilities in Cloud Computing Environment”, 2017 IEEE 10th International Conference on Cloud Computing, Vol. 4, pp. 743-748, 2017.
[11] M. Shamim Hossaina, Ghulam Muhammadb, Wadood Abdulc, Biao Songd , B. B. Gupta, “Cloud-assisted secure video transmission and sharing framework for smart cities”, ELSEVIER Future Generation Computer Systems, Vol. 4, pp. 45-57, 2017.
[12] Varun Mahajan, Sateesh K Peddoju, “Deployment of Intrusion Detection System in Cloud: A Performance-Based Study”, Vol. 5, pp. 140-153, 2017.
[13] Mohammad Taghi Adili, Amin Mohammadi, Mohammad Hossein Manshaei, and Mohammad Ashiqur Rahman, “Cost-Effective Security Management for Clouds: A Game-Theoretic Deception Mechanism”, Vol. 5, pp. 98-106, 2017.
[14] Tara Salman, Deval Bhamare, Aiman Erbad, Raj Jain, Mohammed Samaka, “Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments”, 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing, Vol. 3, pp. 97-103, 2017.