Distributed Virtual Network Embedding System with Historical Archives and Set-Based Particle Swarm Optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

19 Scopus Citations
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Author(s)

  • An Song
  • Wei-Neng Chen
  • Tianlong Gu
  • Huaqiang Yuan
  • Jun Zhang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8600712
Pages (from-to)927-942
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume51
Issue number2
Online published3 Jan 2019
Publication statusPublished - Feb 2021

Abstract

Virtual network embedding (VNE) is an important problem in network virtualization for the flexible sharing of network resources. While most existing studies focus on centralized embedding for VNE, distributed embedding is considered more scalable and suitable for large-scale scenarios, but how virtual resources can be mapped to substrate resources effectively and efficiently remains a challenging issue. In this paper, we devise a distributed VNE system with historical archives (HAs) and metaheuristic approaches. First, we introduce metaheuristic approaches to each delegation of the distributed embedding system as the optimizer for VNE. Compared to the heuristic-based greedy algorithms used in existing distributed embedding approaches, which are prone to be trapped in local optima, metaheuristic approaches can provide better embedding performance for these distributed delegations. Second, an archive-based strategy is also introduced in the distributed embedding system to assist the metaheuristic algorithms. The archives are used to record the up-to-date information of frequently repeated tasks. By utilizing such archives as historical memory, metaheuristic algorithms can further improve embedding performance for frequently repeated tasks. Following this idea, we incorporate the set-based particle swarm optimization (PSO) as the optimizer and propose the distributed VNE system with HAs and set-based PSO (HA-VNE-PSO) system to solve the VNE problem in a distributed way. HA-VNE-PSO is empirically validated in scenarios of different scales. The experimental results verify that HA-VNE-PSO can scale well with respect to substrate networks, and the HA strategy is indeed effective in different scenarios.

Research Area(s)

  • Distributed systems, metaheuristic, particle swarm optimization (PSO), virtual network embedding (VNE)

Citation Format(s)

Distributed Virtual Network Embedding System with Historical Archives and Set-Based Particle Swarm Optimization. / Song, An; Chen, Wei-Neng; Gu, Tianlong et al.

In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51, No. 2, 8600712, 02.2021, p. 927-942.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review