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Application Centric Virtual Machine Placements to Minimize Bandwidth Utilization in Datacenters


An efficient placement of virtual machines (VMs) in a cloud data center is important to maximize the utilization of infrastructure. Most of the existing work maximizes the number of VMs to place on a minimum number of physical machines (PMs) to reduce energy consumption. Recently, big data applications become popular which are mostly hosted on cloud data centers. Big data applications are deployed on multiple VMs and considered data and communication-intensive applications. These applications can consume most of the datacenter bandwidth if VMs do not place close to each other. In this paper, we investigate the use of different VM placement methods to decrease the usage of bandwidth in different sizes of data centers. We implemented and evaluated five different VM placement algorithms. Our comprehensive set of experiments show a significant decrease in bandwidth ranging from 65% to 78% can be achieved using the extended implementations of the knapsack and first fit VM placement methods.



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A. K. Paul, S. K. A., Sahoo, B., & Turuk, A. K. (2014). Application of greedy algorithms to virtual machine distribution across data centers, (pp. 1–6). M. Abdullah, M. (2017). Application level VM placement simulation.

A. Alahmadi, Alnowiser, A., Zhu, M. M., Che, D., & Ghodous, P. (2014). Enhanced first-fit decreasing algorithm for energy-aware job scheduling in cloud. In 2014 International Conference on Computational Science and Computational Intelligence (pp. 69–74). volume 2. U. Bhuvan, Rosenberg, A. L., & Shenoy, P. (2007). Application placement on a cluster of servers. International Journal of Foundations of Computer Science, 18, 1023–1041.

N. Bobroff, Kochut, A., & Beaty, K. (2007). Dynamic placement of virtual machines for managing sla violations. In 2007 10th IFIP/IEEE International Symposium on Integrated Network Management (pp. 119–128).

R. S. Camati, Calsavara, A., & Jr., L. L. (2014). Solving the virtual machine placement problem as a multiple multidimensional knapsack problem, . (pp. 253–260).

M. Chen, Zhang, H., Su, Y. Y., Wang, X., Jiang, G., & Yoshihira, K. (2011). Effective vm sizing in virtualized data centers. In 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops (pp. 594–601).

T. Chen, Gao, X., & Chen, G. (2016). Optimized virtual machine placement with traffic-aware balancing in data center networks. Sci. Program., 2016, 4–.

S. Crago, Dunn, K., Eads, P., Hochstein, L., Kang, D. I., Kang, M., Modium, D., Singh, K., Suh, J., & Walters, J. P. (2011). Heterogeneous cloud computing. In 2011 IEEE International Conference on Cluster Computing (pp. 378–385). J. Dong, Wang, H., & Cheng, S. (2015). Energy-performance tradeoffs in iaas cloud with virtual machine scheduling. China Communications, 12, 155–166.

Ferdaus, Md Hasanul et al. "Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic." Euro-Par 2014 Parallel Processing (2014): 306-317. Crossref. Web.

F. Silva, I. Dutra, & V. Santos Costa (Eds.), EuroPar 2014 Parallel Processing: 20th International Conference, Porto, Portugal, August 25-29, 2014. Proceedings (pp. 306–317). Cham: Springer International Publishing.

Y. Gao, Guan, H., Qi, Z., Hou, Y., & Liu, L. (2013). A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J. Comput. Syst. Sci., 79, 1230–1242. C, Ghribi, Hadji, M., & Zeghlache, D. (2013). Energy efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms. In 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (pp. 671–678).

A. R. Ilkhechi, Korpeoglu, I., & zgr Ulusoy (2015). Network-aware virtual machine placement in cloud data centers with multiple traffic-intensive components. Computer Networks, 91, 508 – 527.

W, Iqbal, Dailey, M. N., & Carrera, D. (2016). Unsupervised learning of dynamic resource provisioning policies for cloud-hosted multitier web applications. IEEE Systems Journal, 10, 1435–1446.

W. Iqbal, Dailey, M. N., Carrera, D., & Janecek, P. (2011). Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Future Generation Computer Systems, 27, 871–879.

C.-F. Kuo, Yeh, T.-H., Lu, Y.-F., & Chang, B.-R. -(2015). Efficient allocation algorithm for virtual machines in cloud computing systems. In Proceedings of the ASE BigData & SocialInformatics 2015 (pp. 48:1–48:6). New York, NY, USA: ACM.

M. Masdari, Nabavi, S. S., & Ahmadi, V. (2016). An overview of virtual machine placement schemes in 12 MUHAMMAD ABDULLAH, ET AL. cloud computing. J. Netw. Comput. Appl., 66, 106–127.

X. Meng, Pappas, V., & Zhang, L. (2010). Improving the scalability of data center networks with trafficaware virtual machine placement. In INFOCOM, 2010 Proceedings IEEE (pp. 1–9). IEEE.

H. Mi, Wang, H., Yin, G., Zhou, Y., Shi, D., & Yuan, L. (2010). Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In 2010 IEEE International Conference on Services Computing (pp. 514–521).

T. Shabeera, Kumar, S. M., Salam, S. M., & Krishnan, K. M. (2017). Optimizing vm allocation and data placement for data-intensive applications in cloud using aco metaheuristic algorithm. Engineering Science and Technology, an International Journal, 20, 616 – 628.

D. Shen, Luo, J., Dong, F., & Zhang, J. (2016). Appbag: Application-aware bandwidth allocation for virtual machines in cloud environment. In 2016 45th International Conference on Parallel Processing (ICPP) (pp. 21–30).

A. Singh, Korupolu, M., & Mohapatra, D. (2008a). Server-storage virtualization: Integration and load balancing in data centers. In 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1–12).

A. Singh, Korupolu, M., & Mohapatra, D. (2008b). Server-storage virtualization: Integration and load balancing in data centers. In 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1–12).

W. Song, Xiao, Z., Chen, Q., & Luo, H. (2014). Adaptive resource provisioning for the cloud using online bin packing. IEEE Transactions on Computers, 63, 2647–2660.

F. Tao, Li, C., Liao, T. W., & Laili, Y. (2016). Bgm-bla: A new algorithm for dynamic migration of virtual machines in cloud computing. IEEE Transactions on Services Computing, 9, 910–925.

T. Thiruvenkadam, & Karthikeyani, V. (2014). An approach to virtual machine placement problem in a datacenter environment based on overloaded resource. International Journal of Computer Science and Mobile Computing, .

M. Wang, Meng, X., & Zhang, L. (2011). Consolidating virtual machines with dynamic bandwidth demand in data centers. In 2011 Proceedings IEEE INFOCOM (pp. 71–75).

T. Wood, Shenoy, P., Venkataramani, A., & Yousif, M. (2009). Sandpiper: Black-box and gray-box resource management for virtual machines. Comput. Netw., 53, 2923–2938


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present

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