SLA Management For Virtual Machine Live Migration Using Machine Learning with Modified Kernel and Statistical Approach

  • M. K. Hassan Future University, Khartoum, Sudan
  • A. Babiker Neelain University, Khartoum, Sudan
  • M. Baker University of Gezira, Sudan
  • M. Hamad Universiti Teknologi Malaysia, Malaysia
Keywords: virtual machine, migration, machine learning, SLA


Application of cloud computing is rising substantially due to its capability to deliver scalable computational power. System attempts to allocate a maximum number of resources in a manner that ensures that all the service level agreements (SLAs) are maintained. Virtualization is considered as a core technology of cloud computing. Virtual machine (VM) instances allow cloud providers to utilize datacenter resources more efficiently. Moreover, by using dynamic VM consolidation using live migration, VMs can be placed according to their current resource requirements on the minimal number of physical nodes and consequently maintaining SLAs. Accordingly, non optimized and inefficient VMs consolidation may lead to performance degradation. Therefore, to ensure acceptable quality of service (QoS) and SLA, a machine learning technique with modified kernel for VMs live migrations based on adaptive prediction of utilization thresholds is presented. The efficiency of the proposed technique is validated with different workload patterns from Planet Lab servers.



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P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt, A. Warfield, “Xen and the art of virtualization”, 19th ACM Symposium on Operating Systems Principles, pp 164-177, 2003

S. Akoush, R. Sohan, A. Rice, A. W. Moore, A. Hopper, “Predicting the Performance of Virtual Machine Migration”, IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems, 2010

C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul, C. Limpach, I. Pratt, A. Warfield, “Live migration of virtual machines”, 2nd Symposium on Networked Systems Design and Implementation, pp 273-286, 2005

A. B. Nagarajan , F. Mueller, C. Engelmann, L. Scott, “Proactive fault tolerance for HPC with Xen virtualization”, 21st Annual International Conference on Supercomputing, pp 23–32, 2007

R. Nathuji, K. Schwan, “Virtual power: Coordinated power management in virtualized enterprise systems”, ACM SIGOPS Operating Systems Review, Vol. 41, No. 6, pp. 265-278, 2007

Y. Song, H. Wang, Y. Li, B. Feng, Y. Sun, “Multi-tiered on-demand resource scheduling for VM-based data center”, 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 148-155, 2009

VMware Inc, VMware distributed power management concepts and use, 2010

A. Beloglazov, R. Buyya.,Adaptive Threshold-Based Approach for Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers, Dept. of Computer Science and Software Engineering, University of Melbourne, 2010

T. C. Ferreto, M. A. S. Netto, R. N. Calheiros, C. A. F. De Rose, “Server consolidation with migration control for virtualized data centers”, Future Generation Compute Systems, Vol. 27, No. 8, pp 1027–1034, 2011

T. Wood, G.Tarasuk-Levin, P. Shenoy, P. Desnoyers, E. Cecchet, M. D. Corner, “Memory buddies: exploiting page sharing for smart co-location in virtualized data centers”, ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, pp. 31-40,2009

T. Hirofuchi, H. Nakada, S. Itoh, S. Sekiguchi, “Reactive consolidation of virtual machines enabled by post copy live migration”, 5th international workshop on Virtualization technologies in distributed computing, pp 11-18, 2011

D. Kakadia, N. Kopri, V. Varma, “Network-aware virtual machine consolidation for large data centers”, 3rd International Workshop on Network-Aware Data Management, 2013

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

M. Sindelar, R. K. Sitaraman, P. Shenoy, “Sharing-aware algorithms for virtual machine co location”, AMC 23rd symposium on Parallelism in algorithms architectures, pp. 367-378, 2011

A. Beloglazov, “Energy-efficient management of virtual machines in data centers for cloud computing”, PhD Thesis, Department of Computer Science, Melbourne University, 2013

T. Chen, X. Gao, G. Chen, “Optimized Virtual Machine Placement with Traffic-Aware Balancing in Data Center Networks”, Scientific Programming, Vol. 2016, Article ID 3101658, 2016

Z. Zhou, Z. Hu, K. Li, “Virtual Machine Placement Algorithm for Both Energy-Awareness and SLA Violation Reduction in Cloud Data Centers”, Scientific Programming, Vol. 2016, Article ID 5612039, 2016

M. Khalaf Alla H. M., A. Babiker, M. B. M. Amien, M. Hamad, “Review in cloud based next generation telecommunication network”, Jurnal Teknology, Vol. 78, No. 6, pp. 51–57, 2016

R. Timofeev, Classification and Regression Trees (CART). Theory and Applications, MSc Thesis, Humboldt University, Berlin, 2004

H. Yu, S. Kim, “SVM Tutorial: Classification, Regression, and Ranking”, In: Handbook of Natural Computing, pp. 479-506, Springer, 2012

K. Q. Weinberger, L. K. Saul, “Distance Metric Learning for Large Margin Nearest Neighbor Classification”, Journal of Machine Learning Research, Vol. 10, pp. 207-244, 2009

N. Japkowicz, M. Shah, Evaluating Learning Algorithms: A Classification Perspective, Cambridge Press, 2011


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