Network-aware virtual machine assignment method in cloud

被引：0
|
作者：
Lyu S. [1 ]
Xu Y. [1 ]
Zhang T.-B. [1 ]
Li G.-L. [2 ,3 ]
Chi C. [4 ]
机构：
[1] College of Computer Science and Technology, Jilin University, Changchun
[2] State Key Laboratory of Computer Architecture, Institute of Computing Technology of Chinese Academy of Sciences, Beijing
[3] University of Chinese Academy of Sciences, Beijing
[4] College of Software, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 04期
关键词：
Cloud computing; Computer software; Satisfiability; Virtual machine assignment;
D O I：
10.13229/j.cnki.jdxbgxb20190568
中图分类号：
学科分类号：
摘要：
SAT-based methods for network-aware virtual machine assignment problem in cloud environment are proposed in this paper. These methods can solve five kinds of virtual machine problems including that with task priorities or with mandatory tasks. Each of the five kinds of virtual machine assignment problems is converted into a corresponding SAT-like problem and is solved by the existing SAT-like solver. It could solve the larger scale of the five kinds of virtual machine assignment problems more efficiently. Experimentally, the proposed methods are demonstrated to solve the five kinds of virtual machine assignment problems effectively. In contrast to the existing algorithms in the five scenarios mentioned above, the proposed methods can solve a larger scale of data with higher efficiency. © 2020, Jilin University Press. All right reserved.
引用
收藏
页码：1455 / 1464
页数：9
相关论文
共 17 条
  • [1] Wang W, Jiang Y, Wu W., Multiagent-based resource allocation for energy minimization in cloud computing systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 2, pp. 205-220, (2016)
  • [2] Darrous J, Ibrahim S, Zhou A C, Et al., Nitro: network-aware virtual machine image management in geo-distributed clouds, The 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing(CCGRID), pp. 553-562, (2018)
  • [3] Jennings B, Stadler R., Resource management in clouds: survey and research challenges, Journal of Network and Systems Management, 23, 3, pp. 567-619, (2015)
  • [4] Alicherry M, Lakshman T V., Optimizing data access latencies in cloud systems by intelligent virtual machine placement, IEEE INFOCOM, pp. 647-655, (2013)
  • [5] Malekimajd M, Movaghar A., Minimizing data access latencies for virtual machine assignment in cloud systems, IEEE Transactions on Services Computing, (2017)
  • [6] Kuo J J, Yang H H, Tsai M J., Optimal approximation algorithm of virtual machine placement for data latency minimization in cloud systems, IEEE INFOCOM Toronto, pp. 1303-1311, (2014)
  • [7] Helene C, Louet G L, Menaud J M., Virtual machine placement for hybrid cloud using constraint programming, IEEE 23rd International Conference on Parallel and Distributed Systems, pp. 326-333, (2017)
  • [8] Shabeera T P, Kumar S D M, Salam S M, Et al., Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm, Engineering Science and Technology, an International Journal, 20, 2, pp. 616-628, (2017)
  • [9] Ashraf A, Porres I., Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system, International Journal of Parallel Emergent and Distributed Systems, 33, 1, pp. 103-120, (2018)
  • [10] Een N, Biere A., Effective preprocessing in SAT through variable and clause elimination, International Conference on Theory and Applications of Satisfiability Testing, pp. 61-75, (2005)
12