Optimal network locality in distributed virtualized data-centers

  • Authors:

  • Jimmy Leblet;Zhe Li;Gwendal Simon;Di Yuan

  • Affiliations:

  • Institut Telecom, Telecom Bretagne, Brest, France and Université Lyon 3, Lyon, France;Institut Telecom, Telecom Bretagne, Brest, France;Institut Telecom, Telecom Bretagne, Brest, France;Linköping University, Linköping, Sweden

  • Venue:
  • Computer Communications
  • Year:
  • 2011

Quantified Score

Hi-index 0.24

Visualization

Abstract

Cost efficiency is a key aspect in deploying distributed service in networks within decentralized service delivery architectures. In this paper, we address this aspect from an optimization and algorithmic standpoint. The research deals with the placement of service components to network sites, where the performance metric is the cost for acquiring components between the sites. The resulting optimization problem, which we refer to as the k-Component Multi-site Placement Problem, is applicable to service distribution in a wide range of communication networking scenarios. We provide a theoretical analysis of the problem's computational complexity, and develop an integer programming model for providing reference results for performance benchmarking. On the algorithmic side, we present four approaches: an algorithm with approximation guarantee and three heuristics algorithms. The first heuristic is derived from graph theory on domatic partition. The second heuristic, built on intuition, admits distributed computation. The third heuristic emphasizes on fairness in cost distribution among the sites. We report simulation results for sets of networks where cost is represented by round-trip time (RTT) originating from real measurements. For small networks, the integer model is used to study algorithm performance in terms of optimality. Large networks are used to compare the algorithms relatively to each other. Among the algorithms, the heuristic based on intuition has close-to-optimal performance, and the fairness heuristic achieves a good balance between single-site cost and the overall one. In addition, the experiments demonstrate the significance of optimization for cost reduction in comparison to a the random allocation strategy.