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HMGOWM: A Hybrid Decision Mechanism for Automating Migration of Virtual Machines - 2018

HMGOWM: A Hybrid Decision Mechanism for Automating Migration of Virtual Machines

Research Area:  Cloud Computing


Large-scale data centers have been widely used for cloud services, and the stability of various cloud services has received additional attention from users. Although service disruptions are not as catastrophic as they once were, their impact might be more extensive than before. These outages may trigger the migration of virtual machines (VMs) located in the failure node. However, the access time of each VM is random, unlike the accident time, which can be predicted. This means that traditional migration caused by service interruptions may result in a large number of unwanted migrations, regardless of the users downtime experience. Migration is an expensive process in terms of the resources needed as well as the degradation of application performance during migration. A balance between the recovery time of the service (to minimize the migration resulting from a given placement) and the downtime experience of the users (to minimize the impact of access interruptions) is needed. In this paper, we propose HMGOWM, a hybrid decision-making mechanism for automating the migration of VMs. Our proposed mechanism extends the original VM migration performance cost model, greatly reducing the downtime experience of the users. To achieve high performance and a good load balance, a multi-objective monitoring system for both VMs and physical machine nodes and an adaptive VM migration-scheduling scheme for the OpenStack cloud platform are proposed. Extensive experiment results indicate that the downtime experienced by users can be efficiently reduced and that the implementation of HMGOWM outperforms the original scheduling of the OpenStack cloud platform.


Author(s) Name:  Ronghui Cao; Zhuo Tang; Kenli Li and Keqin Li

Journal name:  IEEE Transactions on Services Computing ( Early Access )

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TSC.2018.2873694

Volume Information:  Page(s): 1 - 1