Optimized Renewable Energy Use in Green Cloud Data Centers

  • Minxian Xu
  • Adel N. ToosiEmail author
  • Behrooz Bahrani
  • Reza Razzaghi
  • Martin Singh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)


The huge energy consumption of cloud data centers not only increases costs but also carbon emissions associated with such data centers. Powering data centers with renewable or green sources of energy can reduce brown energy use and consequently carbon emissions. However, powering data centers with these energy sources is challenging, as they are variable and not available at all times. In this work, we formulate the microservices management problem as finite Markov Decision Processes (MDP) to optimise renewable energy use. By dynamically switching off non-mandatory microservices and scheduling battery usage, upon the user’s preference, our proposed method makes a trade-off between the workload execution and brown energy consumption. We evaluate our proposed method using traces derived from two real workloads and real-world solar data. Simulated experiments show that, compared with baseline algorithms, our proposed approach performs up to 30% more efficiently in balancing the brown energy usage and workload execution.



This work is partially supported by Monash Infrastructure Research Seed Fund Grant and FIT Early Career Researcher Seed Grant.


  1. 1.
    Buyya, R., Srirama, S.N., et al.: A manifesto for future generation cloud computing: research directions for the next decade. ACM Comput. Surv. 51(5), 105:1–105:38 (2018)CrossRefGoogle Scholar
  2. 2.
    Cianfrani, A., Eramo, V., Listanti, M., Polverini, M., Vasilakos, A.V.: An OSPF-integrated routing strategy for QoS-aware energy saving in IP backbone networks. IEEE Trans. Netw. Service Manag. 9(3), 254–267 (2012)CrossRefGoogle Scholar
  3. 3.
    Farahnakian, F., Pahikkala, T., Liljeberg, P., Plosila, J., Hieu, N.T., Tenhunen, H.: Energy-aware VM consolidation in cloud data centers using utilization prediction model. IEEE Trans. Cloud Comput. 7(2), 524–536 (2019). CrossRefGoogle Scholar
  4. 4.
    Goiri, Í., Katsak, W., Le, K., Nguyen, T.D., Bianchini, R.: Parasol and GreenSwitch: managing datacenters powered by renewable energy. In: ACM SIGARCH Computer Architecture News, vol. 41, pp. 51–64. ACM (2013)Google Scholar
  5. 5.
    Han, Z., Tan, H., Chen, G., Wang, R., Chen, Y., Lau, F.C.M.: Dynamic virtual machine management via approximate Markov decision process. In: Proceedings of the 35th Annual IEEE International Conference on Computer Communications (INFOCOM), pp. 1–9 (2016)Google Scholar
  6. 6.
    Jiang, D., Xu, Z., Liu, J., Zhao, W.: An optimization-based robust routing algorithm to energy-efficient networks for cloud computing. Telecommun. Syst. 63(1), 89–98 (2016)CrossRefGoogle Scholar
  7. 7.
    Liu, H., et al.: Thermal-aware and DVFS-enabled big data task scheduling for data centers. IEEE Trans. Big Data 4(2), 177–190 (2018)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Liu, Z., et al.: Renewable and cooling aware workload management for sustainable data centers. In: ACM SIGMETRICS Performance Evaluation Review, vol. 40, pp. 175–186. ACM (2012)Google Scholar
  9. 9.
    Shaw, R., Howley, E., Barrett, E.: A predictive anti-correlated virtual machine placement algorithm for green cloud computing. In: 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing, pp. 267–276. IEEE (2018)Google Scholar
  10. 10.
    Shen, H., Chen, L.: Distributed autonomous virtual resource management in datacenters using finite-Markov decision process. IEEE/ACM Trans. Netw. 25(6), 3836–3849 (2017)CrossRefGoogle Scholar
  11. 11.
    Terefe, M.B., Lee, H., Heo, N., Fox, G.C., Oh, S.: Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing. Pervasive Mob. Comput. 27, 75–89 (2016)CrossRefGoogle Scholar
  12. 12.
    Toosi, A.N., Qu, C., de Assunção, M.D., Buyya, R.: Renewable-aware geographical load balancing of web applications for sustainable data centers. J. Netw. Comput. Appl. 83, 155–168 (2017)CrossRefGoogle Scholar
  13. 13.
    Xu, M., Buyya, R.: Energy efficient scheduling of application components via brownout and approximate Markov decision process. In: Maximilien, M., Vallecillo, A., Wang, J., Oriol, M. (eds.) ICSOC 2017. LNCS, vol. 10601, pp. 206–220. Springer, Cham (2017). Scholar
  14. 14.
    Xu, M., Buyya, R.: Brownout approach for adaptive management of resources and applications in cloud computing systems: a taxonomy and future directions. ACM Comput. Surv. 52(1), 8:1–82:7 (2019)CrossRefGoogle Scholar
  15. 15.
    Zhang, Y., Wang, Y., Wang, X.: GreenWare: greening cloud-scale data centers to maximize the use of renewable energy. In: Kon, F., Kermarrec, A.-M. (eds.) Middleware 2011. LNCS, vol. 7049, pp. 143–164. Springer, Heidelberg (2011). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Minxian Xu
    • 1
    • 2
  • Adel N. Toosi
    • 2
    Email author
  • Behrooz Bahrani
    • 3
  • Reza Razzaghi
    • 3
  • Martin Singh
    • 4
  1. 1.Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
  2. 2.Faculty of Information TechnologyMonash UniversityClaytonAustralia
  3. 3.Department of Electrical and Computer Systems EngineeringMonash UniversityClaytonAustralia
  4. 4.School of Earth, Atmosphere and EnvironmentMonash UniversityClaytonAustralia

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