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A Pliant-based Virtual Machine Scheduling Solution to Improve the Energy Efficiency of IaaS Clouds

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Abstract

Recently cloud computing is facing increasing attention as it is applied in many business scenarios by advertising the illusion of infinite resources towards its customers. Nevertheless, it raises severe issues with energy consumption: the higher levels of quality and availability require irrational energy expenditures. This paper proposes Pliant system-based virtual machine scheduling approaches for reducing the energy consumption of cloud datacenters. We have designed a CloudSim-based simulation environment for task-based cloud applications to evaluate our proposed solution, and applied industrial workload traces for our experiments. We show that significant savings can be achieved in energy consumption by our proposed Pliant-based algorithms, in this way a beneficial trade-off can be reached by IaaS providers between energy consumption and execution time.

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References

  1. Buyya, R., Yeo, C.S., 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 

  2. vor dem Berge, M., Da Costa, G., Kopecki, A., Oleksiak, A., Pierson, J-M., Piontek, T., Volk, E., Wesner, S.: Modeling and simulation of data center energy-efficiency in CoolEmAll Energy Efficient Data Centers. Lect. Notes Comput. Sci. 73, 2536 (2012)

    Google Scholar 

  3. Berral, J.L., Goiri, I., Nou, R., Julia, F., Guitart, J., Gavalda, R., Torres, J.: Towards energy-aware scheduling in data centers using machine learning.. In: procedings of the 1st Internatinal Conference on Energy-Efficient Computing and Networking, pp. 215–224 (2010)

  4. 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. Software: Practice and Experience (SPE) 41(1), 23–50 (2011)

    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 Exper. 24(13), 1397–1420 (2012)

    Article  Google Scholar 

  6. Bacso, G., Visegradi, A., Kertesz, A., Nemeth, Zs.: On efficiency of multi-job grid allocation based on statistical trace data. J. Grid Computing 12(1), 169–186 (2014)

    Article  Google Scholar 

  7. Schubert, L., Jeffery, K.: Advances in Clouds Research in Future Cloud Computing, Report from the Cloud Computing Expert Working Group Meeting. Cordis (Online), BE: European Commission. Online: http://cordis.europa.eu/fp7/ict/ssai/docs-/future-cc-2may-finalreport-experts.pdf (2012)

  8. Dombi, J.: A general class of fuzzy operators, the de morgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy Set. Syst., 8 (1982)

  9. Dombi, J.: Pliant system. In: IEEE International Conference on Intelligent Engineering System Proceedings, Budapest, Hungary (1997)

  10. Dombi, J.D., Kertesz, A.: Avanced Scheduling Techniques with the Pliant System for High-Level Grid Brokering. Communications in Computer and Information Science (CCIS), vol. 129, pp. 173–185. Springer, Berlin Heidelberg (2011)

    Google Scholar 

  11. Kertesz, A.: Characterizing Cloud Federation Approaches. In: Mahmood, Z. (ed.) In book: Cloud Computing - Challenges, Limitations and R&D Solutions, Springer Series on Computer Communications and Networks, pp. 277–296 (2014)

  12. Kertesz, A., Kecskemeti, G., Oriol, M., Kotcauer, P., Acs, S., Rodriguez, M., Merce, O., Marosi, A.Cs., Marco, J., Franch, X.: Enhancing federated cloud management with an integrated service monitoring approach. J. Grid Computing 11(4), 699–720 (2013)

    Article  Google Scholar 

  13. Lefvre, L., Orgerie, A.: Towards energy aware reservation infrastructure for large-scale experimental distributed systems. Parallel Process. Lett. 19(3), 419–433 (2009)

    Article  MathSciNet  Google Scholar 

  14. Pugliese, A., Talia, D., Yahyapour, R.: Modeling and supporting grid scheduling. J. Grid Computing 6(2), 195–213 (2008)

    Article  Google Scholar 

  15. Kertesz, A., Kacsuk, P.: GMBS: a new middleware service for making grids interoperable. Futur. Gener. Comput. Syst. 16, 542–553 (2010)

    Article  Google Scholar 

  16. Park, K.S., Pai, V.S.: CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Oper. Syst. Rev. 40(1), 74 (2006)

    Article  Google Scholar 

  17. Lucas-Simarro, J.L., Moreno-Vozmediano, R., Montero, R.S., Llorente, I.M.: Scheduling strategies for optimal service deployment across multiple clouds. Futur. Gener. Comput. Syst. doi:10.1016/j.future.2012.01.007,2012.

  18. Cioara, T., Anghel, I., Salomie, I., Copil, G., Moldovan, D., Kipp, A.: Energy aware dynamic resource consolidation algorithm for virtualized service centers based on reinforcement learning.. In: proceedings of the 10th International Symposium on Parallel and Distributed Computing, pp. 163–169 (2011)

  19. Feller, E., Rilling, L., Morin, C., Lottiaux, R., Leprince, D.: Snooze: A scalable, fault-tolerant and distributed consolidation manager for large-scale clusters. In: IEEE/ACM Int’l Conference on Green Computing and Communications (GreenCom), pp. 125–132 (2010)

  20. Cardosa et al.: Shares and utilities based power consolidation in virtualized server environments. In: Proceedings IEEE/IFIP Conference Integrated Management (2009)

  21. Lee, Y.C., Wang, C., Zomaya, A.Y., Zhou, B.B.: Profit-driven service request scheduling in clouds.. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), 2010, pp. 15–24 (2010)

  22. Berral, J., Goiri, I., Nou, R., Juli, F., Guitart, J., Gavald, R., Torres, J.: Towards energy-aware scheduling in data centers using machine learning. In: 1st International Conference on Energy-Efficiency Computing and Networking, p 2010. Passau, Germany

  23. Feller, E., Rohr, C., Margery, D., Morin, C.: Energy management in IaaS Clouds: a holistic approach.. In: IEEE International Conference on Cloud Computing (CLOUD), Honolulu, Hawaii, USA (2012)

  24. Salleh, S., Sanugi, B., Jamaluddin, H.: Fuzzy logic model for dynamic multiprocessor scheduling. Matematika 15(2), 95–109 (1999)

    Google Scholar 

  25. Sotiriadis, S., Bessis, N., Antonopoulos, N.: Towards inter-cloud simulation performance analysis: exploring service-oriented benchmarks of clouds in SimIC.. In: Proceedings of the 27th International Conference onAdvanced Information Networking and Applications Workshops (WAINA’13), Barcelona, Spain, pp. 765–771 (2013)

  26. Verma, A., Ahuja, P., Neogi, A.: pMapper: power and migration cost aware application placement in virtualized systems. In book: Middleware, ppp. 243–264 (2008)

  27. Benyi, A., Dombi, J.D., Kertesz, A.: Energy-aware VM Scheduling in IaaS Clouds using Pliant logic.. In: proceedings of the 4th International Conference on Cloud Computing and Services Science (CLOSER’14), Barcelona, Spain (2014)

  28. Prezi Inc.: “Scale Contest” website. http://prezi.com/scale/, Accessed on 23 April, 2013. (2013)

  29. SPEC website: www.spec.org, Accessed on 12 April, 2014. (2014)

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Kertesz, A., Dombi, J.D. & Benyi, A. A Pliant-based Virtual Machine Scheduling Solution to Improve the Energy Efficiency of IaaS Clouds. J Grid Computing 14, 41–53 (2016). https://doi.org/10.1007/s10723-015-9336-9

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  • DOI: https://doi.org/10.1007/s10723-015-9336-9

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