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MVMM: Data Center Scheduler Algorithm for Virtual Machine Migration

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Advanced Information Networking and Applications (AINA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 926))

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Abstract

Migration algorithms and conception of data centers have significant impact on cost and service qualities. Researchers are looking to find effective solutions for Virtual Machine (VM) migration in data centers to reduce power consumption under the given quality of service constraints. This paper presents a model for the consistency of VM migration in a data center by Dynamic Bayesian Networks (DBN). The novelty of our work is to construct a DBN according to the probabilistic dependencies between the parameters in order to make decisions about the corresponding placement of VM in the distributed data centers. In addition, to evaluate the proposed model, we add a new scheduler algorithm inspiring from the DBN model into GreenCloud simulator, called MVMM (Minimization of Virtual Machine Migration), which provides a VM score to make decisions about the matching placement of VMs in order to allow a minimization of future VMs migrations. The results reveal that the use of the proposed approach can reduce energy consumption until 8% compared to other scheduler algorithms.

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References

  1. Cheng, J., Greiner, R., Kelly, J., Bell, D.A., Liu, W.: Learning Bayesian networks from data: an information-theory based approach. Artif. Intell. 137, 43 (2002)

    Article  MathSciNet  Google Scholar 

  2. Gonzales, C., Jouve, N.: Learning Bayesian networks structure using Markov networks. In: Proceedings of PGM, pp. 147–154 (2006)

    Google Scholar 

  3. Deviren, M., Daoudi, K.: Structural learning of dynamic Bayesian networks in speech recognition. In: Proceedings of EUROSPEECH (2001)

    Google Scholar 

  4. Ghahramani, Z.: Learning dynamic Bayesian networks. In: Adaptive Processing of Sequences and Data Structures, pp. 168–197. Springer, Heidelberg (1998)

    Google Scholar 

  5. Lähdesmäki, H., Shmule, I.: Learning the structure of dynamic Bayesian networks from time series and steady state measurements. Mach. Learn. 71, 185–217 (2008)

    Article  Google Scholar 

  6. Robinson, J.W., Hartemink, A.J.: Learning non-stationary dynamic Bayesian networks. J. Mach. Learn. Res. 11, 3647–3680 (2010)

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  8. Borgetto, D.: Allocation et Réallocation de services pour économies d’énergie dans les clusters et les clouds’, IRIT Institut de recherche en info de Toulouse (2013)

    Google Scholar 

  9. Emir, I., Dobrisa, D.: Grid infrastructure monitoring system based on nagios. In: Workshop on Grid monitoring, pp. 23–28. ACM (2009)

    Google Scholar 

  10. Kliazovich, D., Bouvry, P., Khan, S.U.: GreenCloud: a packet-level simulator of energy-aware cloud computing data centers (2010)

    Google Scholar 

  11. Choi, J., Govindan, S., Urgaonkar, B., Sivasubramaniam, A.: Profiling, prediction and capping of power consumption in consolidated environments. In: IEEE International Symposium on Modeling, Analysis and Simulation of Computers and Telecommunication Systems (MASCOTS) (2008). Print ISSN: 1526-7539

    Google Scholar 

  12. Wang, Y., Keller, E., Biskeborn, B., Merwe, J., Rexford, J.: Virtual routers on the move: live router migration as a network-management primitive. IEEE SIGCOMM (2008)

    Google Scholar 

  13. Nathuji, R., Schwan, K., Somani, A., Joshi, Y.: VPM tokens: virtual machine-aware power budgeting in datacenters. Cluster Comput. 12(2), 189–203 (2009)

    Article  Google Scholar 

  14. Sonneck, J., Chandra, A.: Virtual putty: reshaping the physical footprint of virtual machines. In: HotCloud Workshop in Conjunction with USENIX Annual Technical Conference (2009)

    Google Scholar 

  15. Clark, C., et al.: Live migration of Virtual machines. In: Proceedings of the 2nd Symposium on Networked Systems Design & Implementation (NSDIS). USENIX Association, CA, USA (2005)

    Google Scholar 

  16. RedHat Enterprise Virtualization: System Scheduler. Data sheet. http://www.redhat.com/f/pdf/rhev/doc060-

  17. Sharkh, M.A., Shali, A., Ouda, A.: Optimal and suboptimal resource allocation techniques in cloud computing data centers. J. Cloud Comput.: Adv. Syst. Appl. 6, 6 (2017)

    Google Scholar 

  18. Guzek, M., Kliazovich, D., Bouvry, P.: HEROS: energy-efficient load balancing for heterogeneous data centers. In: IEEE 8th International Conference on Cloud Computing (2015). Print ISSN: 2159-6182

    Google Scholar 

  19. Saha, S., Deogun, J., Xu, L.: Energy models driven green routing for data centers. In: 2012 IEEE Global Communications Conference, GLOBECOM, pp. 2529–2534 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Prince Research Laboratory, ISITCom of Hammam Sousse, University of Sousse, Sousse, Tunisia

    Nawel Kortas & Habib Youssef

Authors
  1. Nawel Kortas
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  2. Habib Youssef
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Corresponding author

Correspondence to Nawel Kortas .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Advanced Sciences, Hosei University, Koganei-Shi, Tokyo, Japan

    Makoto Takizawa

  3. Department of Computer Science, Technical University of Catalonia, Barcelona, Barcelona, Spain

    Fatos Xhafa

  4. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Kortas, N., Youssef, H. (2020). MVMM: Data Center Scheduler Algorithm for Virtual Machine Migration. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_87

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