On Virtual Network Reconfiguration in Hybrid Optical/Electrical Datacenter Networks

Hybrid optical/electrical datacenter networks (HOE-DCNs) build inter-rack networks with both electrical Ethernet switches and optical cross-connects (OXCs), and have been considered as a promising DCN architecture. However, to adapt to the dynamic network environment, the reconfiguration of virtual...

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Veröffentlicht in:Journal of lightwave technology 2020-12, Vol.38 (23), p.6424-6436
Hauptverfasser: Zhao, Sicheng, Zhu, Zuqing
Format: Artikel
Sprache:eng
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Zusammenfassung:Hybrid optical/electrical datacenter networks (HOE-DCNs) build inter-rack networks with both electrical Ethernet switches and optical cross-connects (OXCs), and have been considered as a promising DCN architecture. However, to adapt to the dynamic network environment, the reconfiguration of virtual networks (VNTs) in an HOE-DCN still faces the unique difficulty that the HOE-DCN's topology can change because of the one-to-one connectivity of OXCs. To the best of our knowledge, this problem still has not been fully explored. In this article, we address this problem, and consider how to achieve effective VNT reconfiguration in an HOE-DCN such that the IT resource usages in racks can be re-balanced with the migration of virtual machines (VMs). We first formulate a mixed integer linear programming (MILP) to describe the VNT reconfiguration. Then, we solve the problem with two steps, 1) obtaining the VM migration schemes to balance the loads on racks and 2) determining the reconfiguration schemes of related virtual links (VLs) and the OXC. For the first step, we propose a polynomial-time approximation algorithm by leveraging linear relaxation. Then, we tackle the optimization of the second step by developing an algorithm that involves a linear-time dynamic programming and an integer linear programming (ILP). To solve the ILP time-efficiently, we propose another polynomial-time approximation algorithm based on Lagrangian relaxation. Our simulations confirm the effectiveness of the proposed approximation algorithms and verify that the overall procedure including them outperforms the existing approach.
ISSN:0733-8724
1558-2213
DOI:10.1109/JLT.2020.3016775