Advertisement

Bi-objective Heterogeneous Consolidation in Cloud Computing

  • Luis-Angel Galaviz-Alejos
  • Fermín Armenta-Cano
  • Andrei Tchernykh
  • Gleb Radchenko
  • Alexander Yu. Drozdov
  • Oleg Sergiyenko
  • Ramin Yahyapour
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 796)

Abstract

In this paper, we address the problem of power-aware Virtual Machines (VMs) consolidation considering resource contention. Deployment of VMs can greatly influence host performance, especially, if they compete for resources on insufficient hardware. Performance can be drastically reduced and energy consumption increased. We focus on a bi-objective experimental evaluation of scheduling strategies for CPU and memory intensive jobs regarding the quality of service (QoS) and energy consumption objectives. We analyze energy consumption of the IBM System x3650 M4 server, with optimized performance for business-critical applications and cloud deployments built on IBM X-Architecture. We create power profiles for different types of applications and their combinations using SysBench benchmark. We evaluate algorithms with workload traces from Parallel Workloads and Grid Workload Archives and compare their non-dominated Pareto optimal solutions using set coverage and hyper volume metrics. Based on the presented case study, we show that our algorithms can provide the best energy and QoS trade-offs.

Keywords

Virtual machine Consolidation Energy aware scheduling SLA violations Green cloud 

References

  1. 1.
    Cook, G.: How clean is your cloud. Catal. Energy Revolut. 52, 1–52 (2012)Google Scholar
  2. 2.
    Greenpeace International: Make IT Green: Cloud Computing and Its Contribution to Climate Change, pp. 1–12. Greenpeace International, Amsterdam (2010)Google Scholar
  3. 3.
    Varasteh, A., Goudarzi, M.: Server consolidation techniques in virtualized data centers: a survey. IEEE Syst. J. 11(2), 772–783 (2015).  https://doi.org/10.1109/JSYST.2015.2458273 CrossRefGoogle Scholar
  4. 4.
    Tchernykh, A., Schwiegelsohn, U., Talbi, E., Babenko, M.: Towards understanding uncertainty in cloud computing with risks of confidentiality, integrity, and availability. J. Comput. Sci. (2016).  https://doi.org/10.1016/j.jocs.2016.11.011 Google Scholar
  5. 5.
    Farahnakian, F., Ashraf, A., Pahikkala, T., Liljeberg, P., Plosila, J., Porres, I., Tenhunen, H.: Using ant colony system to consolidate VMs for green cloud computing. IEEE Trans. Serv. Comput. 8, 187–198 (2015).  https://doi.org/10.1109/TSC.2014.2382555 CrossRefGoogle Scholar
  6. 6.
    Tchernykh, A., Pecero, J.E., Barrondo, A., Schaeffer, E.: Adaptive energy efficient scheduling in Peer-to-Peer desktop grids. Futur. Gener. Comput. Syst. 36, 209–220 (2014).  https://doi.org/10.1016/j.future.2013.07.011 CrossRefGoogle Scholar
  7. 7.
    Maziku, H., Shetty, S.: Network aware VM migration in cloud data centers. In: 2014 3rd GENI Research and Educational Experiment Workshop, pp. 25–28 (2014).  https://doi.org/10.1109/GREE.2014.18
  8. 8.
    Maziku, H., Shetty, S.: Towards a network aware VM migration: evaluating the cost of VM migration in cloud data centers. In: 2014 IEEE 3rd International Conference on Cloud Networking (CloudNet). pp. 114–119. IEEE (2014)Google Scholar
  9. 9.
    Wu, Q., Ishikawa, F.: Heterogeneous virtual machine consolidation using an improved grouping genetic algorithm. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, pp. 397–404. IEEE (2015)Google Scholar
  10. 10.
    Nesmachnow, S., Iturriaga, S., Dorronsoro, B., Tchernykh, A.: Multiobjective energy-aware workflow scheduling in distributed datacenters. In: Gitler, I., Klapp, J. (eds.) ISUM 2015. CCIS, vol. 595, pp. 79–93. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-32243-8_5 CrossRefGoogle Scholar
  11. 11.
    Armenta-Cano, F.A., Tchernykh, A., Cortes-Mendoza, J.M., Yahyapour, R., Drozdov, A.Y., Bouvry, P., Kliazovich, D., Avetisyan, A., Nesmachnow, S.: Min_c: heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention. Program. Comput. Softw. 43, 204–215 (2017).  https://doi.org/10.1134/S0361768817030021 MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hongyou, L., Jiangyong, W., Jian, P., Junfeng, W., Tang, L.: Energy-aware scheduling scheme using workload-aware consolidation technique in cloud data centres. China Commun. 10, 114–124 (2013).  https://doi.org/10.1109/CC.2013.6723884 CrossRefGoogle Scholar
  13. 13.
    Yang, J.S., Liu, P., Wu, J.J.: Workload characteristics-aware virtual machine consolidation algorithms. In: CloudCom 2012 – Proceedings of 2012 4th IEEE International Conference on Cloud Computing Technology and Science, pp. 42–49 (2012).  https://doi.org/10.1109/CloudCom.2012.6427540
  14. 14.
    Combarro, M., Tchernykh, A., Kliazovich, D., Drozdov, A., Radchenko, G.: Energy-aware scheduling with computing and data consolidation balance in 3-tier data center. In: 2016 International Conference on Engineering and Telecommunication (EnT), pp. 29–33. IEEE (2016)Google Scholar
  15. 15.
    Nath, A.R., Kansal, A., Govindan, S., Liu, J., Suman, N.: PACMan: performance aware virtual machine consolidation. In: 10th International Conference on Autonomic Computing, ICAC 2013, San Jose, CA, USA, pp. 83–94, 26–28 June 2013Google Scholar
  16. 16.
    Verboven, S., Vanmechelen, K., Broeckhove, J.: Network aware scheduling for virtual machine workloads with interference models. IEEE Trans. Serv. Comput. 8, 617–629 (2015).  https://doi.org/10.1109/TSC.2014.2312912 CrossRefGoogle Scholar
  17. 17.
    Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B., Alba, E.: A cellular genetic algorithm for multiobjective optimization. In: Proceedings of Workshop on Nature inspired cooperative strategies for optimization, NICSO 2006, pp. 25–36 (2006)Google Scholar
  18. 18.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).  https://doi.org/10.1109/4235.996017 CrossRefGoogle Scholar
  19. 19.
    Feitelson, D.G., Tsafrir, D., Krakov, D.: Experience with using the parallel workloads archive. J. Parallel Distrib. Comput. 74(10), 2967–2982 (2014).  https://doi.org/10.1016/j.jpdc.2014.06.013 CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Tchernykh, A., Lozano, L., Schwiegelshohn, U., Bouvry, P., Pecero, J.E., Nesmachnow, S., Drozdov, A.Y.: Online bi-objective scheduling for IaaS clouds ensuring quality of service. J. Grid Comput. 14, 5–22 (2016).  https://doi.org/10.1007/s10723-015-9340-0 CrossRefGoogle Scholar
  22. 22.
    Durillo, J.J., Nebro, A.J., Alba, E.: The jMetal framework for multi-objective optimization: design and architecture. Evol. Comput. 5467, 18–23 (2010).  https://doi.org/10.1109/CEC.2010.5586354 Google Scholar
  23. 23.
    Durillo, J.J., Nebro, A.J.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42, 760–771 (2011).  https://doi.org/10.1016/j.advengsoft.2011.05.014 CrossRefGoogle Scholar
  24. 24.
    Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. http://www.tik.ee.ethz.ch/sop/publicationListFiles/zitz1999a.pdf, (1999)
  25. 25.
    Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. Ser. B. 91, 201–213 (2002).  https://doi.org/10.1007/s101070100263 MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.CICESE Research CenterEnsenadaMexico
  2. 2.South Ural State UniversityChelyabinskRussia
  3. 3.Moscow Institute of Physics and Technology, State UniversityMoscowRussia
  4. 4.Universidad Autónoma de Baja CaliforniaMexicaliMexico
  5. 5.University of GöttingenGöttingenGermany

Personalised recommendations