IOT based visualization of weightage based static task scheduling algorithm in datacenter

  • Authors

    • Preethi M
    • Kayalvizhi Jayavel
    2018-03-19
    https://doi.org/10.14419/ijet.v7i2.8.10478
  • Cloud Computing, Resource starving, Shortest Job First, Weightage queues, First Come First Serve, Scheduling, Cloudsim, IOT

  • Cloud computing has raised majorly to provide everything as a service and also for scaling the resources and utilizing the resources in an effective way. This paper aims to propose a scheduling algorithm which allocates static tasks to the resources effectively without making any tasks starve for the resources for long time. In SJF algorithm, the shortest tasks will be executed initially, and the largest tasks will keep on starving for the resources to be allocated. The proposed algorithm handles such a situation effectively by adding the jobs under different weightage queues and then scheduling them in an SJF order. This gives priority to even largest job. In this paper a framework is proposed, which fetches data from Amazon SDB storage and the processing of data based on proposed algorithm occurs in a cloudsim and finally the results are visualized through an IOT mobile device. The comparison is also made for First Come First Serve (FCFS), which is a default scheduling algorithm and the proposed algorithm.

  • References

    1. [1] Abirami S.P and Shalini Ramanathan, “Linear Scheduling Strategy for Resource Allocation in Cloud Environmentâ€, International Journal on Cloud Computing: Services and Architecture(IJCCSA), Vol.2, No.1, February 2012.

      [2] Amdani, S. Y., and S. R. Jadhao. "Novel hybrid cost-priority based scheduling in cloud environment." Recent Advances and Innovations in Engineering (ICRAIE), 2016 International Conference on. IEEE, 2016.

      [3] Bhawna Taneja, "An empirical study of most fit max-min and priority task scheduling algorithms in cloud computing", Computing Communication & Automation (ICCCA) 2015 International Conference on, pp. 664-667, 2015

      [4] Ding, Ding, Xiaocong Fan, and Siwei Luo. "User-oriented cloud resource scheduling with feedback integration." The Journal of Supercomputing 72.8 (2016): 3114-3135.

      [5] Fang Y., Wang F., Ge J. (2010) A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing. In: Wang F.L., Gong Z., Luo X., Lei J. (eds) Web Information Systems and Mining. WISM 2010. Lecture Notes in Computer Science, vol 6318. Springer, Berlin, Heidelberg

      [6] Filip, Ion-Dorinel, et al. "Microservices Scheduling Model over Heterogeneous Cloud-Edge Environments as Support for IoT Applications." IEEE Internet of Things Journal (2018).

      [7] Khalil, Khaled M., et al. "Multiply-Sectioned Bayesian Network for Multi-Agent Learning Based Meta Resources Scheduling in CloudSim" Intelligent Computing and Information Systems (ICICIS), IEEE ,2017"

      [8] Li, Zhongjin, et al. "Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds." IEEE Transactions on Services Computing (2017).

      [9] Madhumathi, Ramasamy, et al. "Clustering Based User Preference Resource Scheduling in Cloud Computing." International Conference on Smart Trends for Information Technology and Computer Communications. Springer, Singapore, 2016.

      [10] Rawat, Pradeep Singh, et al. "Power consumption analysis across heterogeneous data center using CloudSim." Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on. IEEE, 2016.

      [11] Salehi, M.A. and Buyya, R. ‘‘Adapting market-oriented scheduling policies for cloud computing’’, Proceedings of the 10th Int’l Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2010), Busan, Korea,pp. 351–362 (2010).

      [12] S. Selvarani, G.S. Sadhasivam, “Improved cost-based algorithm for task scheduling in Cloud computingâ€, Computational Intelligence and Computing Research (ICCIC), pp.1-5, 2010.

      [13] Sharma, Murli Manohar, and Anju Bala. "Survey paper on workflow scheduling algorithms used in cloud computing." International Journal of Information & Computation Technology 4 (2014): 997-1002.

      [14] Sidhu, Harmanbir Singh. "Comparative analysis of scheduling algorithms of Cloudsim in cloud computingâ€, International Journal of Computer Applications (0975 – 8887) Volume 97– No.16, July 2014.

      [15] Simão, José, and Luís Veiga. "Partial utility-driven scheduling for flexible SLA and pricing arbitration in clouds." IEEE transactions on Cloud Computing 4.4 (2016): 467-480.

      [16] The NIST definition of cloud computing, NIST special publication 800-145.

      [17] Tsai, Chun-Wei, et al. "A hyper-heuristic scheduling algorithm for cloud." IEEE Transactions on Cloud Computing 2.2 (2014): 236-250.

      [18] Vijindra and Sudhir Shenai. A, “Survey of Scheduling Issues in Cloud Computingâ€, 2012, Elsevier Ltd.

      [19] Wen, Zhenyu, et al. "Dynamically partitioning workflow over federated clouds for optimising the monetary cost and handling run-time failures." IEEE Transactions on Cloud Computing (2017).

      [20] Zhangjun Wu1, 2, Xiao Liu2, Zhiwei Ni1, Dong Yuan2, Yun Yang,†A Market-Oriented Hierarchical Scheduling Strategy in Cloud Workflow Systems†in JSC2010.

      [21] S.V.Manikanthan and D.Sugandhi “ Interference Alignment Techniques For Mimo Multicell Based On Relay Interference Broadcast Channel †International Journal of Emerging Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353 Volume- 7 ,Issue 1 –MARCH 2014.

      [22] T. Padmapriya, V.Saminadan, “Performance Improvement in long term Evolution-advanced network using multiple imput multiple output techniqueâ€, Journal of Advanced Research in Dynamical and Control Systems, Vol. 9, Sp-6, pp: 990-1010, 2017.

  • Downloads

  • How to Cite

    M, P., & Jayavel, K. (2018). IOT based visualization of weightage based static task scheduling algorithm in datacenter. International Journal of Engineering & Technology, 7(2.8), 439-443. https://doi.org/10.14419/ijet.v7i2.8.10478