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Energy-efficient Dynamic Virtual Machine Management In Data Centers

ENERGY-EFFICIENT DYNAMIC VIRTUAL MACHINE MANAGEMENT IN DATA CENTERS

  • Project Code :
  • TCRENS19_31
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ENERGY-EFFICIENT DYNAMIC VIRTUAL MACHINE MANAGEMENT IN DATA CENTERS

Energy-Efficient Dynamic Virtual Machine Management in Data Centers

Abstract:

Efficient virtual machine (VM) management can dramatically reduce energy consumption in information 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 can't adapt to the dynamic nature of VMs' resource demands. Most approaches in the latter class are heuristic and lack theoretical performance guarantees. In this, we formulate the dynamic VM management as a large-scale Markov call method (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 tend to 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 employment traces show that MadVM achieves vital 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.

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