NetDEO: automating network design, evolution, and optimization

  • Authors:
  • Zhenyu Wu;Yueping Zhang;Vishal Singh;Guofei Jiang;Haining Wang

  • Affiliations:
  • College of William and Mary, Williamsburg, VA;NEC Laboratories America, Inc., Princeton, NJ;NEC Laboratories America, Inc., Princeton, NJ;NEC Laboratories America, Inc., Princeton, NJ;College of William and Mary, Williamsburg, VA

  • Venue:
  • Proceedings of the 2012 IEEE 20th International Workshop on Quality of Service
  • Year:
  • 2012

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Abstract

With the ever-increasing number and complexity of applications deployed in data centers, the underlying network infrastructure can no longer sustain such a trend and exhibits several problems, such as resource fragmentation and low bisection bandwidth. In pursuit of a real-world applicable data center network (DCN) optimization approach that continuously maintains balanced network performance with high cost effectiveness, we design a topology independent resource allocation and optimization approach, NetDEO. Based on a swarm intelligence optimization model, NetDEO improves the scalability of the DCN by relocating virtual machines (VMs) and matching resource demand and availability. NetDEO is capable of (1) incrementally optimizing an existing VM placement in a data center; (2) deriving optimal deployment plans for newly added VMs; and (3) providing hardware upgrade suggestions and allowing the DCN to evolve as the workload changes over time. We evaluate the performance of NetDEO using realistic workload traces and simulated large-scale DCN under various topologies.