Energy-Efficient Dynamic Virtual Machine Management in Data Centers

Zhenhua Han, Haisheng Tan, Rui Wang, Guihai Chen, Yupeng LI, Francis Chi Moon Lau

Research output: Contribution to journalArticlepeer-review

Abstract

Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs' resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs' resource demands. Most approaches in the latter category are heuristic and lack theoretical performance guarantees. In this paper, we formulate the dynamic VM management as a large-scale Markov decision process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, we show that MadVM can be implemented in a distributed system with at most two times of the optimal migration cost. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage, and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms.
Original languageEnglish
Pages (from-to)344-360
Number of pages16
JournalIEEE/ACM Transactions on Networking
Volume27
Issue number1
Early online date25 Jan 2019
DOIs
Publication statusPublished - Feb 2019

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

  • Cloud computing
  • resource management
  • energy efficiency
  • Markov decision process

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