GMDS Algorithm based on OpenStack
Article No.: 28, Pages 1 - 7
Abstract
With the rapid development of cloud computing, more and more users choose to deploy applications on virtual machines on the cloud platform. And OpenStack is one of the mainstream cloud platforms in cloud computing. The load imbalance of cloud platform is a concern. Live migration of virtual machines is an effective way to achieve load balancing and optimize resource utilization. With the rapid expansion of the scheduling domain of the cloud platform, the traditional centralized migration strategy begins to lack reliability and scalability. In this paper, we propose a new VM dynamic scheduling algorithm based on the grey Markov prediction model. In our algorithm, the gray Markov prediction model is used to predict the state of the node load information, so as to cooperate with the VM dynamic scheduling mechanism to properly migrate the VM, and set the upper and lower thresholds of the node to achieve load balancing and reduce energy consumption. The experimental results show that the GMDS algorithm achieves the reduction of energy consumption by implementing load balancing of the entire system and reducing the number of running nodes, which are superior to the existing migration strategy.
References
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Index Terms
- GMDS Algorithm based on OpenStack
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Published In
December 2018
460 pages
ISBN:9781450366250
DOI:10.1145/3302425
Copyright © 2018 ACM.
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In-Cooperation
- The Hong Kong Polytechnic: The Hong Kong Polytechnic University
- City University of Hong Kong: City University of Hong Kong
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 December 2018
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- Research-article
- Research
- Refereed limited
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- the Defence Advance Research Foundation of China under Grants
- the Chongqing Research Program of Basic Research Frontier Technology
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ACAI 2018
ACAI 2018: 2018 International Conference on Algorithms, Computing and Artificial Intelligence
December 21 - 23, 2018
Sanya, China
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ACAI '18 Paper Acceptance Rate 76 of 192 submissions, 40%;
Overall Acceptance Rate 173 of 395 submissions, 44%
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