Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement

TLDR

The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. The study proposes traffic‑aware virtual machine placement to enhance data‑center network scalability without altering network architecture or routing protocols. The authors formulate VM placement as an optimization problem, prove its hardness, and develop a two‑tier approximate algorithm that efficiently solves large instances by assigning high‑bandwidth VM pairs to proximate hosts and aligning traffic patterns with communication distances. Using production traffic traces, the algorithm achieves significant performance improvement over existing general methods that ignore traffic patterns and network characteristics.

Abstract

The scalability of modern data centers has become a practical concern and has attracted significant attention in recent years. In contrast to existing solutions that require changes in the network architecture and the routing protocols, this paper proposes using traffic-aware virtual machine (VM) placement to improve the network scalability. By optimizing the placement of VMs on host machines, traffic patterns among VMs can be better aligned with the communication distance between them, e.g. VMs with large mutual bandwidth usage are assigned to host machines in close proximity. We formulate the VM placement as an optimization problem and prove its hardness. We design a two-tier approximate algorithm that efficiently solves the VM placement problem for very large problem sizes. Given the significant difference in the traffic patterns seen in current data centers and the structural differences of the recently proposed data center architectures, we further conduct a comparative analysis on the impact of the traffic patterns and the network architectures on the potential performance gain of traffic-aware VM placement. We use traffic traces collected from production data centers to evaluate our proposed VM placement algorithm, and we show a significant performance improvement compared to existing general methods that do not take advantage of traffic patterns and data center network characteristics.