NetDEO: Automating network design, evolution, and optimization

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.