Green and Heuristics-Based Consolidation Scheme for Data Center Cloud Applications

  • Alessandro CarregaEmail author
  • Matteo Repetto
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 766)


The consolidation of resources is one of the most efficient strategies to reduce the power consumption in data centers. Various algorithms have been proposed in order to reduce the total number of required servers and network devices. The practice developed in response to the problem of server sprawl, a situation in which multiple, under-utilized servers (and/or network devices) take up more space and consume more resources than can be justified by their workload; with the effect to power off unused equipment. Generally, consolidation mechanisms consider different parameters related to the services neglecting the specific function of the Virtual Machines (VMs) in the application framework (e.g., core component, backup replica, member of a set of workers for load balancing).

In this work, we developed a new consolidation algorithm that takes into account the particular function of each VM with the aim to apply power saving mechanisms without compromising the desired service level. The results of the simulations show that it is possible to obtain significant values of energy saving. In particular, we show, with different heuristics, the optimal trade-off between service level and power efficiency achieved by the proposed model.


Green Energy-aware Consolidation Virtual machines Cloud applications Optimization Heuristics 



This work was partially supported by the European Commission under the projects ARCADIA (grant no. 607881) and INPUT (grant no. 644672).


  1. 1.
    Hammadi, A., Mhamdi, L.: A survey on architectures and energy efficiency in data center networks. Comput. Commun. 40, 1–21 (2014)CrossRefGoogle Scholar
  2. 2.
    Advanced configuration and power interface specification (2015).
  3. 3.
    Barroso, L.A., Hlzle, U.: The case for energy-proportional computing. Computer 40(12), 33–37 (2007)CrossRefGoogle Scholar
  4. 4.
    Stage, A., Setzer, T.: Network-aware migration control and scheduling of differentiated virtual machine workloads. In: Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing (CLOUD), Vancouver, Canada, 23 May 2009, pp. 9–14 (2009)Google Scholar
  5. 5.
    Voorsluys, W., Broberg, J., Venugopal, S., Buyya, R.: Cost of virtual machine live migration in clouds: a performance evaluation. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) CloudCom 2009. LNCS, vol. 5931, pp. 254–265. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-10665-1_23 CrossRefGoogle Scholar
  6. 6.
    Herbst, N.R., Kounev, S., Reussner, R.: Elasticity in cloud computing: what it is, and what it is not. In: Proceedings of the 10th International Conference on Autonomic Computing (ICAC), San Jose, CA, USA, 24–28 June 2013, pp. 23–27 (2013)Google Scholar
  7. 7.
    Kliazovich, D., Bouvry, P.: DENS: data center energy-efficient network-aware scheduling. In: IEEE/ACM International Conference on Green Computer and Communication & IEEE/ACM International Conference on Cyber, Physical and Social Computing, Hangzhou, China, 18–20 December 2010, pp. 69–75 (2010)Google Scholar
  8. 8.
    Topology and orchestration specification for cloud applications. OASIS Standard, November 2013, version 1.0.
  9. 9.
    Wettinger, J., Breitenbücher, U., Leymann, F.: Standards-based DevOps automation and integration using TOSCA. In: IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), London, UK, 8–11 December 2014, pp. 59–68 (2014)Google Scholar
  10. 10.
    Balalaie, A., Heydarnoori, A., Jamshidi, P.: Microservices architecture enables devops: migration to a cloud-native architecture. IEEE Softw. 33(3), 42–52 (2016)CrossRefGoogle Scholar
  11. 11.
    Dragoni, N., Giallorenzo, S., Lafuente, A.L., Mazzara, M., Montesi, F., Mustafin, R., Safina, L.: Microservices: yesterday, today, and tomorrow. Cornell University Library, arXiv:1606.04036v1, June 2016
  12. 12.
    Srikantaiah, S., Kansal, A., Zhao, F.: Energy aware consolidation for cloud computing. In: Proceedings of the 2008 Conference on Power Aware Computer and Systems (HotPower 2008), San Diego, CA, USA, 7 December 2008 (2008)Google Scholar
  13. 13.
    Mishra, M., Sahoo, A.: On theory of VM placement: anomalies in existing methodologies and their mitigation using a novel vector based approach. In: IEEE International Conference on Cloud Computing (CLOUD), Bombay, Mumbai, India, 4–9 July 2011, pp. 275–282 (2011)Google Scholar
  14. 14.
    Geronimo, G.A., Werner, J., Westphall, C.B., Westphall, C.M., Defenti, L.: Provisioning and resource allocation for green clouds. In: The 12th International Conference on Network (ICN), Seville, Spain, 27 January-1 February 2013Google Scholar
  15. 15.
    Beloglazov, A., Buyya, R.: OpenStack Neat: a framework for dynamic and energy-efficient consolidation of virtual machines in OpenStack clouds. Conc. Comput. Pract. Exp. 27(5), 1310–1333 (2015)CrossRefGoogle Scholar
  16. 16.
    Heller, B., Seetharaman, S., Mahadevan, P., Yiakoumis, Y., Sharma, P., Banerjee, S., McKeown, N.: Elastictree: saving energy in data center networks. In: Proceedings of the 7th USENIX Conference on Network System Design and Implementation, ser. NSDI, p. 17. USENIX Association, Berkeley (2010)Google Scholar
  17. 17.
    Shirayanagi, H., Yamada, H., Kono, K.: Honeyguide: a VM migration-aware network topology for saving energy consumption in data center networks. In: IEEE IEEE Symposium on Computers and Communications (ISCC), Cappadocia, Turkey, 1–4 July 2012, pp. 460–467 (2012)Google Scholar
  18. 18.
    Wang, L., Zhang, F., Vasilakos, A.V., Hou, C., Liu, Z.: Joint virtual machine assignment and traffic engineering for green data center networks. ACM SIGMETRICS Perf. Eval. Rev. 41(3), 107–112 (2013)CrossRefGoogle Scholar
  19. 19.
    Zhang, Y., Ansari, N.: HERO: hierarchical energy opt. for data center networks. IEEE Syst. J. 9(2), 406–415 (2015)CrossRefGoogle Scholar
  20. 20.
    Fang, W., Liang, X., Li, S., Chiaraviglio, L., Xiong, N.: VMPlanner: optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers. Comput. Netw. 57(1), 179–196 (2013)CrossRefGoogle Scholar
  21. 21.
    Dalvandi, A., Gurusamy, M., Chua, K.C.: Time-aware vmflow placement, routing, and migration for power efficiency in data centers. IEEE Trans. Netw. Serv. Man. 12(3), 349–362 (2015)CrossRefGoogle Scholar
  22. 22.
    Padala, P., Shin, K.G., Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A., Salem, K.: Adaptive control of virtualized resources in utility computing environments. In: Proceedings of the 2Nd ACM SIGOPS/EuroSys European Conference on Computer Systems: ser. EuroSys 2007, pp. 289–302. ACM, New York (2007)Google Scholar
  23. 23.
    Mahadevan, P., Sharma, P., Banerjee, S., Ranganathan, P.: Energy aware network operations. In: Proceedings of the 28th IEEE International Conference on Computer Communication Workshops, ser. INFOCOM 2009, pp. 25–30. IEEE Press, Piscataway (2009)Google Scholar
  24. 24.
    Optaplanner - constraint satisfaction solver (java, open source) (2016).
  25. 25.
    Russell, S.J., Norvig, P., Intelligence, A.: A Modern Approach, 2nd edn. Pearson Education, Upper Saddle River (2003)Google Scholar
  26. 26.
    Glover, F.: Tabu search part I. ORSA J. Comput. 1(3), 190–206 (1989)CrossRefzbMATHGoogle Scholar
  27. 27.
    Glover, F.: Tabu searchpart II. ORSA J. Comput. 2(1), 4–32 (1990)CrossRefzbMATHGoogle Scholar
  28. 28.
    Khachaturyan, A., Semenovsovskaya, S., Vainshtein, B.: The thermodynamic approach to the structure analysis of crystals. Acta Crystallogr. Sect. A 37(5), 742–754 (1981)CrossRefGoogle Scholar
  29. 29.
    Burke, E.K., Bykov, Y.: The late acceptance hill-climbing heuristic. Europ. J. Opt. Res. 258(1), 70–78 (2017)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Al-Fares, M., Loukissas, A., Vahdat, A.: A scalable, commodity data center network architecture. SIGCOMM Comput. Commun. Rev. 38(4), 63–74 (2008)CrossRefGoogle Scholar
  31. 31.
    Niranjan Mysore, R., Pamboris, A., Farrington, N., Huang, N., Miri, P., Radhakrishnan, S., Subramanya, V., Vahdat, A.: Portland: a scalable fault-tolerant layer 2 data center network fabric. SIGCOMM Comput. Commun. Rev. 39(4), 39–50 (2009)CrossRefGoogle Scholar
  32. 32.
    Mahadevan, P., Sharma, P., Banerjee, S., Ranganathan, P.: A power benchmarking framework for network devices. In: Fratta, L., Schulzrinne, H., Takahashi, Y., Spaniol, O. (eds.) NETWORKING 2009. LNCS, vol. 5550, pp. 795–808. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-01399-7_62 CrossRefGoogle Scholar
  33. 33.
    Nam, T.M., Thanh, N.H., Thu, N.Q., Hieu, H.T., Covaci, S.: Energy-aware routing based on power profile of devices in data center networks using SDN. In: 2015 12th International Conference on Electrical Engineering/Electronics Computer, Telecommunication and nformation Technology (ECTI-CON), pp. 1–6, June 2015Google Scholar
  34. 34.
    Benson, T., Anand, A., Akella, A., Zhang, M.: Understanding data center traffic characteristics. SIGCOMM Comput. Commun. Rev. 40(1), 92–99 (2010)CrossRefGoogle Scholar
  35. 35.
    Benson, T., Akella, A., Maltz, D.A.: Network traffic characteristics of data centers in the wild. In: Proceedings of the 10th ACM SIGCOMM Conference on International Measures, ser. IMC 2010, pp. 267–280. ACM, New York (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.DITEN – University of GenoaGenoaItaly
  2. 2.CNIT – Research Unit of GenoaGenoaItaly

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