An over-utilization avoidance host selection scheme for affording workload of migrated VM

During the conservation of energy in Cloud Computing, server consolidation comes as a well-known process to achieve maximum utilization of resources with the least number of servers active. In this process of server consolidation, the Virtual Machine (VM) migration acts as the backbone where VMs are made to transfer the workload from one server to the other, provided Quality of Service (QoS) does not get compromised. While performing the act of migration, the selection of host on which the VM workload is to be transferred needs to be chosen intelligently such that performance is not degraded. This paper henceforth proposes a host selection scheme for the migration process avoiding over-utilization of a host by using median of medians algorithm as base.

[1]  Hai Jin,et al.  Live Virtual Machine Migration via Asynchronous Replication and State Synchronization , 2011, IEEE Transactions on Parallel and Distributed Systems.

[2]  Sangyoon Oh,et al.  Sercon: Server Consolidation Algorithm using Live Migration of Virtual Machines for Green Computing , 2011 .

[3]  Amin Jula,et al.  Cloud computing service composition: A systematic literature review , 2014, Expert Syst. Appl..

[4]  Mahdi Aiash,et al.  Secure Live Virtual Machines Migration: Issues and Solutions , 2014, 2014 28th International Conference on Advanced Information Networking and Applications Workshops.

[5]  Feng Xia,et al.  A survey on virtual machine migration and server consolidation frameworks for cloud data centers , 2015, J. Netw. Comput. Appl..

[6]  Maziar Goudarzi,et al.  Structure-aware online virtual machine consolidation for datacenter energy improvement in cloud computing , 2015, Comput. Electr. Eng..

[7]  Feng Zhao,et al.  Energy aware consolidation for cloud computing , 2008, CLUSTER 2008.

[8]  Adrian Ramirez-Nafarrate,et al.  Policy-Based Agents for Virtual Machine Migration in Cloud Data Centers , 2013, 2013 IEEE International Conference on Services Computing.

[9]  Meenu Chawla,et al.  A Technical Review for Efficient Virtual Machine Migration , 2013, 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies.

[10]  Pangfeng Liu,et al.  Server Consolidation Algorithms with Bounded Migration Cost and Performance Guarantees in Cloud Computing , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[11]  Nagarajan Kandasamy,et al.  Power and performance management of virtualized computing environments via lookahead control , 2008, 2008 International Conference on Autonomic Computing.

[12]  Xiaohong Jiang,et al.  Live Migration of Multiple Virtual Machines with Resource Reservation in Cloud Computing Environments , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[13]  P. Santhi Thilagam,et al.  Heuristics based server consolidation with residual resource defragmentation in cloud data centers , 2015, Future Gener. Comput. Syst..

[14]  Hai Jin,et al.  Developing resource consolidation frameworks for moldable virtual machines in clouds , 2014, Future Gener. Comput. Syst..

[15]  Marius Hillenbrand,et al.  High performance cloud computing , 2013, Future Gener. Comput. Syst..

[16]  James She,et al.  A virtual machine consolidation framework for MapReduce enabled computing clouds , 2012, 2012 24th International Teletraffic Congress (ITC 24).

[17]  Michela Meo,et al.  Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers , 2013, IEEE Transactions on Cloud Computing.

[18]  Jie Zheng,et al.  Pacer: A Progress Management System for Live Virtual Machine Migration in Cloud Computing , 2013, IEEE Transactions on Network and Service Management.

[19]  Junzhou Luo,et al.  Stochastic modeling of dynamic right-sizing for energy-efficiency in cloud data centers , 2015, Future Gener. Comput. Syst..

[20]  Rajkumar Buyya,et al.  Energy Efficient Allocation of Virtual Machines in Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.