A kernel search algorithm for virtual machine consolidation problem in cloud computing

Virtual machine consolidation refers to the process of reallocating virtual machines across a set of target servers. It can be formulated as a mixed integer linear programming problem, which is known to be NP-hard. In this paper, we propose a kernel search (KS) heuristic algorithm based on hard vari...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 79; no. 17; pp. 19277 - 19296
Main Authors: Luo, Jiang-Yao, Yuan, Jian-Hua
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2023
Springer Nature B.V
Subjects:
Algorithms
Cloud computing
Compilers
Computer centers
Computer Science
Consolidation
Costs
Efficiency
Fixing
Heuristic
Heuristic methods
Integer programming
Interpreters
Linear programming
Mixed integer
Optimization
Processor Architectures
Programming Languages
Search algorithms
Variables
Virtual environments
Cloud computing
Mixed integer linear programming
Kernel search
Virtual machine consolidation
Heuristic algorithm
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary:Virtual machine consolidation refers to the process of reallocating virtual machines across a set of target servers. It can be formulated as a mixed integer linear programming problem, which is known to be NP-hard. In this paper, we propose a kernel search (KS) heuristic algorithm based on hard variable fixing to quickly obtain high-quality solutions for large-scale virtual machine consolidation problems (VMCPs). Since existing variable fixing strategies in KS algorithms may result in some VMCP instances being infeasible, our proposed KS algorithm employs a more efficient strategy to choose a set of fixed variables based on their corresponding reduced costs. Our numerical results on VMCP instances demonstrate that our proposed KS algorithm significantly outperforms mixed integer linear programming solvers in terms of CPU time. Moreover, our proposed strategy of variable fixing improves the efficiency of the KS algorithm significantly, with negligible degradation in solution quality.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-023-05406-w