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.


Cloud computing Task scheduling Gravitational search algorithm Harmony search Makespan Cost 


  1. 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)CrossRefGoogle Scholar
  2. 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)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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).
  6. 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. 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)CrossRefGoogle Scholar
  8. 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. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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. 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

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSathyabama Institute of Science and TechnologyChennaiIndia
  2. 2.Department of Information TechnologySt. Joseph’s College of EngineeringChennaiIndia

Personalised recommendations