A Resource Usage Prediction-Based Energy-Aware Scheduling Algorithm for Instance-Intensive Cloud Workflows

  • Zhibin Wang
  • Yiping WenEmail author
  • Yu Zhang
  • Jinjun Chen
  • Buqing Cao
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)


The applications of instance-intensive workflow are widely used in e-commerce, advanced manufacturing, etc. However, existing studies normally do not consider the problem of reducing energy consumption by utilizing the characters of instance-intensive workflow applications. This paper presents a resource usage Prediction-based Energy-Aware scheduling algorithm, named PEA. Technically, this method improves the energy efficiency of instance-intensive cloud workflow by predicting resources utilization and the strategies of batch processing and load balancing. The efficiency and effectiveness of the proposed algorithm are validated by extensive experiments.


Energy Instance-intensive Scheduling Cloud workflow Batch processing Prediction 



This paper was supported by National Natural Science Fund of China (No. 61772193, 61402167, 61702181, 61572187, 61873316 and 61872139), Innovation Platform Open Foundation of Hunan Provincial Education Department of China (No. 17K033), Hunan Provincial Natural Science Foundation of China (No. 2017JJ2139, 2017JJ4036, 2016JJ2056 and 2017JJ2098), and the Key projects of Research Fund in Hunan Provincial Education Department of China (No. 15A064).


  1. 1.
    Alizai, M.H., Kunz, G., Landsiedel, O., Wehrle, K.: Promoting power to a first class metric in network simulations. In: International Conference on Architecture of Computing Systems, pp. 1–6 (2010)Google Scholar
  2. 2.
    Lien, C.-H., Liu, M.F., Bai, Y.-W., Lin, C.H., Lin, M.-B.: Measurement by the software design for the power consumption of streaming media servers. In: IEEE Instrumentation and Measurement Technology Conference Proceedings, pp. 1597–1602 (2006)Google Scholar
  3. 3.
    Rahmanian, A.A., Ghobaei-Arani, M., Tofighy, S.: A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment. Future Gener. Comput. Syst. 79, 57–71 (2017)Google Scholar
  4. 4.
    Hsu, C.-H., Slagte, K.D., Chen, S.-C., Chung, Y.-C.: Optimizing energy consumption with activity consolidation in clouds. Inf. Sci. 258, 452–462 (2014)CrossRefGoogle Scholar
  5. 5.
    Liu, J., Jinmin, H.: Dynamic batch processing in workflows: model and implementation. Future Gener. Comput. Syst. 23, 338–347 (2007)CrossRefGoogle Scholar
  6. 6.
    Liu, J., Wen, Y., Li, T., Zhang, X.: A data-operation model based on partial vector space for batch processing in workflow. Concurrency Comput. Pract. Experience 23, 1936–1950 (2011)CrossRefGoogle Scholar
  7. 7.
    Dou, W., Xiaolong, X., Meng, S., Yang, J.: An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data. Concurrency Comput. Pract. Experience 29, e3909 (2016)CrossRefGoogle Scholar
  8. 8.
    Xu, R., Wang, Y., Huang, W., Yang, Y.: Near-optimal dynamic priority scheduling strategy for instance-intensive business workflows in cloud computing. Concurrency Comput. Pract. Experience 29, e4167 (2017)CrossRefGoogle Scholar
  9. 9.
    Rahman, M., Hassan, R., Ranjan, R., Buyya, R.: Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurrency Comput. Pract. Experience 25, 1816–1842 (2013)CrossRefGoogle Scholar
  10. 10.
    Moreno, M., Mirandola, R.: Dynamic power management for QoS-aware applications. Sustain. Comput. Inf. Syst. 3, 231–248 (2013)Google Scholar
  11. 11.
    Ma, Y., Gong, B., Sugihara, R., Gupta, R.: Energy-efficient deadline scheduling for heterogeneous systems. J. Parallel Distrib. Comput. 72, 1725–1740 (2012)CrossRefGoogle Scholar
  12. 12.
    Changtian, Y., Jiong, Y.: Energy-aware genetic algorithms for activity scheduling in cloud computing. In: Chinagrid Conference IEEE, pp. 43–48 (2012)Google Scholar
  13. 13.
    Kim, N., Cho, J., Seo, E.: Energy-credit scheduler: an energy-aware virtual machine scheduler for cloud systems. Future Gener. Comput. Syst. 32, 128–137 (2014)CrossRefGoogle Scholar
  14. 14.
    Yassa, S., Chelouah, R., Hubert, K., Granado, B.: Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. Sci. World J. 2013, 13 (2013)CrossRefGoogle Scholar
  15. 15.
    Tang, X., Chen, C., He, B.: Green-aware workload scheduling in geographically distributed data centers. In: IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp. 82–89 (2013)Google Scholar
  16. 16.
    Cui, L., Zhang, T., Xu, G., Yuan, D.: A scheduling algorithm for multi-tenants instance-intensive workflows. Appl. Math. Inf. Sci. 7, 99–105 (2013)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Li, Z., Ge, J., Haiyang, H., Song, W., Hao, H., Luo, B.: Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds. IEEE Trans. Serv. Comput. 11, 713–726 (2018)CrossRefGoogle Scholar
  18. 18.
    Potts, C.N., Kovalyov, M.Y.: Scheduling with batching: a review. Eur. J. Oper. Res. 120, 228–249 (2000)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Pufahl, L.: Modeling and executing batch activities in business processes. University of Potsdam (2018)Google Scholar
  20. 20.
    Zhang, W., Duan, P., Yang, L.T., Yang, S.: Resource requests prediction in the cloud computing environment with a deep belief network. Softw.: Pract. Experience 47, 473–488 (2017)Google Scholar
  21. 21.
    Kimura, B., Yokoyama, R.S., Miranda, T.O.: Workload regression-based resource provisioning for small cloud providers. In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp. 295–301. IEEE (2016)Google Scholar
  22. 22.
    Ardagna, D., Casolari, S., Colajanni, M.: Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems. J. Parallel Distrib. Comput. 72, 796–808 (2012)CrossRefGoogle Scholar
  23. 23.
    Roy, N., Dubey, A., Gokhale, A.: Efficient autoscaling in the cloud using predictive models for workload forecasting. In: 2011 IEEE 4th International Conference on Cloud Computing, pp. 500–507. IEEE (2011)Google Scholar
  24. 24.
    Sunirma, K., Manna, M.M., Mukherjee, N.: Prediction-based instant resource provisioning for cloud applications. In: IEEE/ACM International Conference on Utility and Cloud Computing, pp. 597–602. IEEE (2015)Google Scholar
  25. 25.
    Rahmanian, A.A., Ghobaei-Arani, M., Tofighy, S.: A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment. Future Gener. Comput. Syst. 79, 54–71 (2017)CrossRefGoogle Scholar
  26. 26.
    Caglar, F., Gokhale, A.: iOverbook: intelligent resource-overbooking to support soft real-time applications in the cloud. In: IEEE International Conference on Cloud Computing, pp. 538–545. IEEE (2014)Google Scholar
  27. 27.
    Wang, Z., Wen, Y., Chen, J., Cao, B., Wang, F.: Towards energy-efficient scheduling with batch processing for instance-intensive cloud workflows. In: International Symposium on Parallel and Distributed Processing with Applications (2018)Google Scholar
  28. 28.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Experience 41, 23–50 (2011)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Zhibin Wang
    • 1
    • 2
  • Yiping Wen
    • 1
    • 2
    Email author
  • Yu Zhang
    • 1
    • 2
  • Jinjun Chen
    • 1
    • 3
  • Buqing Cao
    • 1
    • 2
  1. 1.School of Computer Science and EngineeringHunan University of Science and TechnologyXiangtanChina
  2. 2.Key Laboratory of Knowledge Processing and Networked ManufacturingHunan University of Science and TechnologyXiangtanChina
  3. 3.Swinburne Data Science Research InstituteSwinburne University of TechnologyMelbourneAustralia

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