Energy efficient data center resources management using beam search algorithm

Sergii Telenyk,

Oleksander Rolik,

Eduard Zharikov,

Yevhenii Serdiuk

Modern data centres consume large amounts of power resulting in high levels of carbon dioxide emission. The data centre is a virtual environment in which the workload is performed by virtual machines. A widely used technique to decrease data centre power consumption is to consolidate the virtual machines using a minimal number of physical servers. The authors propose a two-stage method to solve the virtual machine consolidation problem in cloud data centres. The proposed method is programmed in C# to evaluate it and to perform modelling using Google cluster-usage traces. The proposed method enables powering off nearly fifty percent of the previously selected physical servers by using an acceptable number of migrations of virtual machines.
Słowa kluczowe: cloud computing, virtualisation, virtual machine consolidation, local beam search

[1] Lopez Pires F., Baran B., A virtual machine placement taxonomy, [in:] Proc. of the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2015, 159–168.
[2] Ahmad R.W., Gani A., Hamid S.H.A., Shiraz M., Yousafzai A., Xia F., A survey on virtual machine migration and server consolidation frameworks for cloud data centers, Journal of Network and Computer Applications, Vol. 52, 2015, 11–25.
[3] Telenyk S., Zharikov E., Rolik O., An approach to virtual machine placement in cloud data centers, [in:] Proc. of the 2016 International Conference Radio Electronics & Info Communications (UkrMiCo) 11–16 September, Kyiv, Ukraine, 2016, 1–6.
[4] Lopez Pires F., Baran B., Multi-objective virtual machine placement with service level agreement: A memetic algorithm approach, [in:] Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, IEEE Computer Society, 2013, 203–210.
[5] Saber T., Ventresque A., Brandic I., Thorburn J., Murphy L., Towards a Multi-objective VM Reassignment for Large Decentralised Data Centres, 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), Limassol, 2015, 65–74.
[6] Eucalyptus community, (access: 10.12.2017).
[7] Lee S., Panigrahy R., Prabhakaran V., Ramasubrahmanian V., Talwar K., Uyeda L., Wieder U., Validating heuristics for virtual machines consolidation, Microsoft Research, MSR-TR-2011-9, 2011.
[8] Sharma B., Chudnovsky V., Hellerstein J.L., Rifaat R., Das C.R., Modeling and synthesizing task placement constraints in google compute clusters, In Proceedings of the 2nd ACM Symposium on Cloud Computing (SOCC), 2011.
[9] Mark C.C., Niyato D., Chen-Khong T., Evolutionary optimal virtual machine placement and demand forecaster for cloud computing, [in:] IEEE International Conference on Advanced Information Networking and Applications (AINA), 2011, 348–355.
[10] Gao, Y., Guan, H., Qi, Z., Hou, Y., Liu, L., A multi-objective ant colony system algorithm for virtual machine placement in cloud computing, Journal of Computer and System Sciences, Vol. 79, No. 8, 2013, 1230–1242.
[11] Ferreto, T., De Rose C., Heiss, H.U., Maximum migration time guarantees in dynamic server consolidation for virtualized data centers, [in:] Euro-Par 2011 Parallel Processing, Springer 2011, 443–454.
[12] Wu Y., Tang M., Fraser W., A simulated annealing algorithm for energy efficient virtual machine placement, [in:] IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012, 1245–1250.
[13] Reiss C., Wilkes J., Hellerstein J.L., Google cluster-usage traces: format+ schema, Google Inc., Mountain View, CA, USA, Technical Report, 2011.
[14] Zharikov E., Managing data center resources using heuristic search, Problems in programming, Vol. 4, 2017, 16–27.
[15] Limits on Simultaneous Migrations,, last accessed 2018/01/10 (access: 24-12-2017).
[16] Telenyk S., Zharikov E., Rolik O., Consolidation of Virtual Machines Using Stochastic Local Search, Advances in Intelligent Systems and Computing, Springer, 2017, 523–537.