Issue No. 06 - Nov.-Dec. (2017 vol. 10)
Ao Zhou , State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications
Shangguang Wang , State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications
Bo Cheng , State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications
Zibin Zheng , School of Data and Computer ScienceSun Yat-Sen University
Fangchun Yang , State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications
Rong N. Chang , IBM Research
Michael R. Lyu , Department of Computer Science & EngineeringThe Chinese University of Hong Kong
Rajkumar Buyya , Cloud Computing and Distributed Systems (CLOUDS) Lab, Department of Computing and Information Systems, The University of Melbourne, Australia
With rapid adoption of the cloud computing model, many enterprises have begun deploying cloud-based services. Failures of virtual machines (VMs) in clouds have caused serious quality assurance issues for those services. VM replication is a commonly used technique for enhancing the reliability of cloud services. However, when determining the VM redundancy strategy for a specific service, many state-of-the-art methods ignore the huge network resource consumption issue that could be experienced when the service is in failure recovery mode. This paper proposes a redundant VM placement optimization approach to enhancing the reliability of cloud services. The approach employs three algorithms. The first algorithm selects an appropriate set of VM-hosting servers from a potentially large set of candidate host servers based upon the network topology. The second algorithm determines an optimal strategy to place the primary and backup VMs on the selected host servers with k-fault-tolerance assurance. Lastly, a heuristic is used to address the task-to-VM reassignment optimization problem, which is formulated as finding a maximum weight matching in bipartite graphs. The evaluation results show that the proposed approach outperforms four other representative methods in network resource consumption in the service recovery stage.
Cloud computing, Servers, Fault tolerant systems, Redundancy, Virtual machining
A. Zhou et al., "Cloud Service Reliability Enhancement via Virtual Machine Placement Optimization," in IEEE Transactions on Services Computing, vol. 10, no. 6, pp. 902-913, 2017.