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GHSA: Task Scheduling in Heterogeneous Cloud

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 26)

Abstract

Cloud Environments provides affords effective distribution of resource on need, which makes depart from others providing splendid performance, scalability, cost efficient and less maintenance. Task Scheduling increases the dynamic allocation of resource to increase performance and decrease the cost. A solution considering makespan and cost, are used as constraints for the optimization problem. A combination of Gravitational search algorithm (GSA) and Harmony search (HS) is used and created a new hybrid algorithm called Gravitational Harmony Search algorithm (GHSA) which produced enormous improvement over other scheduling algorithms. The simulation is proposed in a cloudsim programming environment and results proved the effectiveness of the cost minimizing and makespan parameters. The proposed algorithm works superior over The simulation is proposed in a cloudsim programming environment and results proved the effectiveness of the cost minimizing and makespan parameters.

Keywords

  • Cloud computing
  • Task scheduling
  • Gravitational search algorithm
  • Harmony search
  • Makespan
  • Cost

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References

  1. Zuo, X., Zhang, G., Tan, W.: Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans. Autom. Sci. Eng. 11(2), 564–573 (2014)

    CrossRef  Google Scholar 

  2. Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, T.: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access 3, 2687–2699 (2015)

    CrossRef  Google Scholar 

  3. Somasundaram, T.S., Govindarajan, K.: CLOUDRB: a framework for scheduling and managing high-performance computing (HPC) applications in science cloud. Future Gener. Comput. Syst. 34, 47–65 (2014)

    CrossRef  Google Scholar 

  4. Abdullahi, M., Ngadi, M.A.: Hybrid symbiotic organisms search optimization algorithm for scheduling of tasks on cloud computing environment. PLoS ONE 11(6), e0158229 (2016)

    CrossRef  Google Scholar 

  5. Sreenu, K., Malempati, S. MFGMTS: epsilon constraint-based modified fractional grey wolf optimizer for multi-objective task scheduling in cloud computing. IETE J. Res. 1–15 (2017). https://doi.org/10.1080/03772063.2017.1409087

  6. Zuo, L., Dong, S., Shu, L., Zhu, C., Han, G.: A multiqueue interlacing peak scheduling method based on tasks’ classification in cloud computing. IEEE Syst. J. 2, 1518–1530 (2016)

    Google Scholar 

  7. He, H., Xu, G., Pang, S., Zhao, Z.A.M.T.S.: Adaptive multi-objective task scheduling strategy in cloud computing. China Commun. 13(4), 162–171 (2016)

    CrossRef  Google Scholar 

  8. Krishnadoss, P., Jacob, P.: OCSA: task scheduling algorithm in cloud computing environment. Int. J. Intell. Eng. Syst. 11(4), 271–279 (2018)

    Google Scholar 

  9. Pradeep, K., Jacob, T.P.: A hybrid approach for task scheduling using the cuckoo and harmony search in cloud computing environment. Wirel. Personal Commun. 101, 1–25 (2018)

    CrossRef  Google Scholar 

  10. Pradeep, K., Jacob, T.P.: CGSA scheduler: a multi-objective-based hybrid approach for task scheduling in cloud environment. Inf. Secur. J. A Glob. Perspect. 27(2), 77–91 (2018)

    CrossRef  Google Scholar 

  11. Gobalakrishnan, N., Arun, C.: Opposition learning-based grey wolf optimizer algorithm for parallel machine scheduling in cloud environment. Int. J. Intell. Eng. Syst. 10(1), 186–195 (2017)

    Google Scholar 

  12. Pradeep, K., Jacob, T.P.: Comparative analysis of scheduling and load balancing algorithms in cloud environment. In: Proceedings of International Conference on Control, Instrumentation, Communication and Computational Technologies, pp. 526–531 (2016)

    Google Scholar 

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Pradeep, K., Prem Jacob, T. (2019). GHSA: Task Scheduling in Heterogeneous Cloud. In: Hemanth, J., Fernando, X., Lafata, P., Baig, Z. (eds) International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018. ICICI 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-030-03146-6_140

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