Skip to main content

Adaptive task scheduling strategy in cloud: when energy consumption meets performance guarantee


Energy efficiency of cloud computing has been given great attention more than ever before. One of the challenges is how to strike a balance between minimizing the energy consumption and meeting the quality of services such as satisfying performance and resource availability in a timely manner. Many studies based on the online migration technology attempt to move virtual machine from low utilization of hosts and then switch it off with the purpose of reducing energy consumption. In this paper, we aim to develop an adaptive task scheduling strategy. In particular, we first model the virtual machine energy from the perspective of the cloud task scheduling, then we propose a genetic algorithm to achieve adaptive regulations for different requirements of energy and performance in cloud tasks (E-PAGA). Then we design two types of the fitness function for choosing the next generation with different preferences on energy and performance. As a result, we can adaptively adjust the energy and performance target before assigning the task in cloud, which is able to meet various requirements from different users. From the extensive experiments, we pinpoint several important observations which are useful in configuring real cloud data centers: 1) we prove that guaranteeing the minimum total task time usually leads to low energy consumption to some extent; 2) we must pay the price of the sacrificed performance if only taking into account the energy optimization; 3) we come to the conclusion that there is always an optimal condition of energy-efficiency ratio in the cloud data center, and more importantly the specific conditions of the optimal energy-efficiency ratio can be obtained.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14


  1. Albers, S., Fujiwara, H.: Energy-efficient algorithms. ACM Rev. 53(5), 86–96 (2010)

    Article  Google Scholar 

  2. Barroso, L.A., Holzle, U.: The case for energy-proportional computing. Computer 40(12), 33–37 (2007)

    Article  Google Scholar 

  3. Basilio, S.: Provisioning computational resources using virtual machines and leases. University of Chicago, USA (2010)

    Google Scholar 

  4. Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur. Gener. Comput. Syst. 28(5), 755–768 (2012)

    Article  Google Scholar 

  5. Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurr. Comput. Pract. Exp. 24(13), 1397–1420 (2012)

    Article  Google Scholar 

  6. Beloglazov, A., Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: Proc. of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 826–831. USA (2010)

  7. Buyya, R., Ranjan, R., Calheiros, R.N.: Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: challenges and opportunities. In: Proc. of the IEEE High Performance Computing & Simulation. USA, pp 1–11. (2009)

  8. Buyya, R., Yeo, C., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2009)

    Article  Google Scholar 

  9. Calheiros, R.N, Buyya, R., De Rose, C.A.F.: A heuristic for mapping virtual machines and links in emulation testbeds. In: Proc. of the International Conference on Parallel Processing, pp. 518–525 (2009)

  10. Calheiros, R.N., Ranjan, R., Beloglazov, A.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41(1), 23–50 (2011)

    Article  Google Scholar 

  11. Cardosa, M., Korupolu, M.R., Singh, A.: Shares and utilities based power consolidation in virtualized server environments. In: Proc. of IFIP/IEEE International Symposium on the Integrated Network Management, pp. 327–334 (2009)

  12. Dias, A.H.F., De Vasconcelos, J.: Multiobjective genetic algorithms applied to solve optimization problems. Magn. IEEE Trans. 38(2), 1133–1136 (2002)

    Article  Google Scholar 

  13. Enokido, T., Duolikun, D., Takizawa, M.: An extended improved redundant power consumption laxity-based (EIRPCLB) algorithm for energy efficient server cluster systems. World Wide Web 1–27 (2014)

  14. Fan, X., Weber, W.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. ACM SIGARCH Comput. Archit. News 35(2), 13–23 (2007)

    Article  Google Scholar 

  15. Fu, Z., Sun, X., et al.: Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. IEICE Trans. Commun. 98(1), 190–200 (2015)

    Article  Google Scholar 

  16. Goiri, I., Juli, F., Nou, R., Berral, J., Guitart, J., Torres, J.: Energy-aware scheduling in virtualized datacenters. In: Proc. of Cluster Computing and Workshops, pp. 58–67. (2010)

  17. Guo, B., Zhang, D., Yu, Z., et al.: From the internet of things to embedded intelligence. World Wide Web 16(4), 399–420 (2013)

    Article  Google Scholar 

  18. He, W., Xu, L.: A state-of-the-art survey of cloud manufacturing. Int. J. Comput. Integr. Manuf. 28(3), 239–250 (2015)

    Article  Google Scholar 

  19. Jiao, H., Zhang, J., Li, J.H., et al.: Immune optimization of task scheduling on multidimensional QoS constraints. Clust Comput 18(2), 909–918 (2015)

    Article  Google Scholar 

  20. Kolodziej, J., Khan, S.U., Zomaya, A.Y.: A Taxonomy of Evolutionary Inspired Solutions for Energy Management in Green Computing: Problems and Resolution Methods. Advances in Intelligent Modeling and Simulation, pp. 215–233. Springer, Berlin (2012)

    Google Scholar 

  21. Laszewski, G.V., Wang L., Younge A.J., He X.: Power-aware scheduling of virtual machines in DVFS-enabled clusters. In: Proc. of Cluster Computing and Workshops, pp. 368–377. (2009)

  22. Li, J., Qiu, M., Ming, Z., et al.: Online optimization for scheduling preemptable tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72(5), 666–677 (2012)

    Article  Google Scholar 

  23. Li, M., Wu, Y., Chen, J.: Cloud resource scheduling using semantic search engine based on improved parallel genetic algorithm. J. Comput. Theor. Nanosci. 12(8), 1669–1676 (2015)

    Article  Google Scholar 

  24. Moore, J.D., Chase, J.S., Ranganathan, P., et al.: Making scheduling “cool”: temperature-aware workload placement in data centers. In: Proc. of the USENIX annual technical conference, General Track, pp. 61–75 (2005)

  25. Neugebauer, R., McAuley, D.: Energy is just another resource: energy accounting and energy pricing in the Nemesis OS. In: Proc. of the Eighth Workshop on Hot Topics in Operating Systems, pp. 67–72. USA (2001)

  26. Ren, Y., Shen, J., Wang, J., et al.: Mutual verifiable provable data auditing in public cloud storage. J. Internet Technol. 16(2), 317–323 (2015)

    Google Scholar 

  27. Serbanescu, V., Pop, F., Cristea, V., et al.: A formal method for rule analysis and validation in distributed data aggregation service. World Wide Web 1–20 (2015)

  28. Sharma, R.K., Bash, C.E., Patel, C.D., et al.: Balance of power: dynamic thermal management for internet data centers. Internet Comput. IEEE 9(1), 42–49 (2005)

    Google Scholar 

  29. ur, Rehman., Z.: User-side QoS forecasting and management of cloud services. World Wide Web, pp. 1–40. (2015)

  30. Verma, A., Ahuja, P., Neogi, A.: pMapper: power and migration cost aware application placement in virtualized systems. In: Middleware, pp. 243–264. Springer, Berlin Heidelberg (2008)

  31. Whaiduzzaman, M., Sookhak, M., Gani, A., et al.: A survey on vehicular cloud computing. J. Netw. Comput. Appl. 40, 325–344 (2014)

    Article  Google Scholar 

  32. Xia, Z., Wang, X., Sun, X., et al.: A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans. Parallel Distrib. Syst. (2015)

  33. Zhan, Z.H., Liu, X.F., Gong, Y.J., et al.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. 47(4), 63 (2015)

    Article  Google Scholar 

  34. Zhao, H.W., Tian, L.W.: Resource schedule algorithm based on artificial fish swarm in cloud computing environment. Appl. Mech. Mater. 635–637, 1614–1617 (2014)

    Article  Google Scholar 

Download references


This research was supported by National Natural Science Foundation of China (61373015, 61300052), the National High Technology Research and Development Program of China (863 Program) (No. 2007AA01Z404), Research Fund for the Doctoral Program of High Education of China (No. 20103218110017), a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the Fundamental Research Funds for the Central Universities, NUAA (No. NP2013307), Funding of Jiangsu Innovation Program for Graduate Education KYLX_0287, the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Yao Shen or Xiaolin Qin.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shen, Y., Bao, Z., Qin, X. et al. Adaptive task scheduling strategy in cloud: when energy consumption meets performance guarantee. World Wide Web 20, 155–173 (2017).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Cloud computing
  • Cloud task scheduling
  • Energy-aware optimization
  • Genetic algorithms