Abstract
In this work, we focus on the stochastic network-aware virtual machine placement (VM) problem in geodistributed data centers (DCs). We consider the uncertainty of the inter-VMs traffic while making placement and migration decisions. First, we propose a stochastic program with the objective of minimizing inter-DCs traffic. Then, we propose an equivalent optimization model using sampling methods and we present a two-step approach to solve the problem. Experiments show the effectiveness of the proposed approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yu, L., Chen, L., Cai, Z., Shen, H., Liang, Y., Pan, Y.: Stochastic load balancing for virtual resource management in datacenters. IEEE Trans. Cloud Comput. PP(99), 1 (2016)
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 Internet Measurement, pp. 267–280 (2010)
Kandula, S., Sengupta, S., Greenberg, A., Patel, P., Chaiken, R.: The nature of data center traffic: measurements & analysis. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, pp. 202–208 (2009)
Beloglazov, A., Buyya, R.: Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst. 24(7), 1366–1379 (2013)
Xiao, Z., Song, W., Chen, Q.: Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 24(6), 1107–1117 (2013)
Gong, Z., Gu, X., Wilkes, J.: Press: Predictive elastic resource scaling for cloud systems. In: International Conference on Network and Service Management, pp. 9–16 (2010)
Shapiro, A., Dentcheva, D., Ruszczynski, A.: Lectures on Stochastic Programming - Modeling and Theory, vol. 16, 2nd edn. SIAM, Philadelphia (2014)
Maguluri, S.T., Srikant, R., Ying, L.: Stochastic models of load balancing and scheduling in cloud computing clusters. In: INFOCOM Proceedings IEEE, pp. 702–710 (2012)
Ghosh, R., Longo, F., Xia, R., Naik, V.K., Trivedi, K.S.: Stochastic model driven capacity planning for an infrastructure-as-a-service cloud. IEEE Trans. Serv. Comput. 7(4), 667–680 (2014)
Wang, M., Meng, X., Zhang, L.: Consolidating virtual machines with dynamic bandwidth demand in data centers. In: INFOCOM Proceedings IEEE, pp. 71–75 (2011)
Jin, H., Pan, D., Xu, J., Pissinou, N.: Efficient VM placement with multiple deterministic and stochastic resources in data centers. In: 2012 IEEE Global Communications Conference, pp. 2505–2510 (2012)
Chase, J., Niyato, D.: Joint optimization of resource provisioning in cloud computing. IEEE Trans. Services Comput. PP(99), 1 (2015)
Corporate Headquarters: Data center networking: enterprise distributed data centers solutions reference nework design. In: Solutions Reference Network Design, Cisco Systems Inc (2003)
Kim, S., Pasupathy, R., Henderson, S.G.: A Guide to Sample Average Approximation. Handbook of Simulation Optimization. International Series in Operations Research & Management Science, pp. 207–243. Springer, New York (2015). doi:10.1007/978-1-4939-1384-8_8
Homem-de Mello, T., Bayraksan, G.: Monte carlo sampling-based methods for stochastic optimization. Surveys Oper. Res. Manag. Sci. 19(1), 56–85 (2014)
IBM Corporation ILOG CPLEX: http://www.ilog.com/products/cplex/. Accessed 04 Feb 2013
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Teyeb, H., Hadj-Alouane, N.B., Tata, S. (2017). Network-Aware Stochastic Virtual Machine Placement in Geo-Distributed Data Centers. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10573. Springer, Cham. https://doi.org/10.1007/978-3-319-69462-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-69462-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69461-0
Online ISBN: 978-3-319-69462-7
eBook Packages: Computer ScienceComputer Science (R0)