JOURNAL ARTICLE

MULTI-OBJECTIVE OPTIMIZATION OF VIRTUAL MACHINE PLACEMENT ON PHYSICAL SERVERS IN CLOUD DATA CENTERS

Abstract

The problem of virtual machine placement on physical servers in cloud data centers is considered. The resource management system has a two-level architecture consisting of global and local controllers. Local controllers analyze the state of the physical servers on which they are located and determine possible underloading, overloading, and overheating states based on the forecast for the next observation window. If one of the listed states is detected, the local controller notifies the global controller, which selects the destination servers to host the virtual machines via migration. It is proposed to place virtual machines based on the criteria of minimum remaining unused resources and violation of SLA agreements. A mathematical formulation of the optimization problem is given, which is equivalent to the known main assignment problem in terms of structure, necessary conditions, and the nature of variables. Reducing the assignment problem to a closed transport problem allowed us to solve the problem of hosting virtual machines under many criteria in real time and significantly increase its dimension in comparison with heuristic algorithms, which makes it possible to maintain the quality of modern cloud services in the conditions of rapid growth of physical and virtual resources of data centers. The developed mathematical formulation of the problem and the results of computational experiments can be included in the mathematical software of virtual machine live migration.

Keywords:
Server Computer science Virtual machine Cloud computing Distributed computing Heuristics Heuristic Optimization problem Mathematical optimization Operating system Algorithm Artificial intelligence Mathematics

Metrics

5
Cited By
0.66
FWCI (Field Weighted Citation Impact)
14
Refs
0.70
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Cybersecurity and Information Systems
Physical Sciences →  Computer Science →  Computer Networks and Communications
Cloud Computing and Resource Management
Physical Sciences →  Computer Science →  Information Systems
Advanced Data Processing Techniques
Physical Sciences →  Engineering →  Control and Systems Engineering
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