Thanks to the rapid development of cloud datacenter, virtual machine (VM) scheduling has become the key to optimizing energy consumption, service-level agreement, network traffic, etc. Focusing on the optimization of server CPU utilization, energy consumption, network traffic, service performance and so on, the current VM scheduling model often fails to recognize the performance interference between VMs as an optimization parameter. In light of the above, this paper proposes a VM scheduling model, considering both server power consumption and VM performance interference, seeking to lower the energy consumption of the datacenter and the interference between VMs. The experimental results demonstrate that the proposed model outshines the other two models in server CPU utilization, energy consumption, and VM process time.
Cloud computing, Energy consumption, VM scheduling, Interference awareness
 S. Govindan, J. Liu, A. Kansal, A. Sivasubramaniam, Cuanta: Quantifying effects of shared on-chip resource interference for consolidated virtual machines, 2011, Acm Symposium on Cloud Computing, New York, USA.
 S. Verboven, K. Vanmechelen, J. Broeckhove, Black box scheduling for resource intensive virtual machine workloads with interference models. 2013, Future Generation Computer Systems, vol. 29, pp. 1871-1884.
 Y. Koh, R. Knauerhase, P. Brett, M. Bowman, Z.H. Wen, C. Pu, An analysis of performance interference effects in virtual environments, 2007, IEEE International Symposium on Performance Analysis of Systems & Software, CA, USA.
 D. Bruneo, A stochastic model to investigate data center performance and QoS in IaaS cloud computing systems, 2014, IEEE Transactions on Parallel and Distributed Systems, vol. 25, pp. 560-569.
 C. Delimitrou, C. Kozyrakis, QoS-aware scheduling in heterogeneous datacenters with paragon, 2013, ACM Transactions on Computer Systems, vol. 31, New York, USA.
 A.O. Ayodele, J. Rao, T.E. Boult, Performance measurement and interference profiling in multi-tenant clouds, 2015, IEEE 8th International Conference on Cloud Computing, New York, USA.
 R.C. Chiang, H.H Huang, TRACON: Interference-aware scheduling for data-intensive applications in virtualized environments, 2014, IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 5, pp. 1-12.
 T.N. Xu, X.F. Sui, Z.C. Yao, J.Y. Ma, Y.G. Bao, L.X. Zhang, Rethinking virtual machine interference in the era of cloud applications, 2013, IEEE International Conference on High Performance Computing and Communications, Zhangjiajie, China.
 A. Beloglazov, J. Abawajy, R. Buyya, Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing, 2012, Future Generation Computer Systems, vol. 28, pp. 755-768.
 J.K. Verma, C.P. Katti, P.C. Saxena, MADLVF: An energy efficient resource utilization approach for cloud computing, 2014, International Journal of Information Technology and Computer Science, vol. 6, no. 7-8, pp. 56-64.
 A. Dalvandi, M. Gurusamy, K.C. Chua, Power-efficient resource-guaranteed VM placement and routing for time-aware data center applications, 2015, International Journal of Computer and Telecommunications Networking, New York, USA, vol. 88, pp. 249-268.