Consolidating virtual machines with dynamic bandwidth demand in data centers

TLDR

Virtualization enables consolidating virtual machines onto fewer servers, but dynamic network bandwidth demands in production data centers make fixed‑value resource allocation ineffective for efficient tight packing. This study formulates VM consolidation as a stochastic bin‑packing problem and proposes an online packing algorithm that guarantees the number of servers used is within (1+ε)(√2+1) of optimal for any ε>0. The algorithm is evaluated through numerical experiments, demonstrating a 30 % reduction in required servers compared to benchmark methods. In a special case the bound improves to (√2+1) of optimal, confirming the algorithm’s effectiveness in reducing server counts.

Abstract

Recent advances in virtualization technology have made it a common practice to consolidate virtual machines(VMs) into a fewer number of servers. An efficient consolidation scheme requires that VMs are packed tightly, yet receive resources commensurate with their demands. However, measurements from production data centers show that the network bandwidth demands of VMs are dynamic, making it difficult to characterize the demands by a fixed value and to apply traditional consolidation schemes. In this work, we formulate the VM consolidation into a Stochastic Bin Packing problem and propose an online packing algorithm by which the number of servers required is within (1+∈)(√2+1) of the optimum for any ∈ >; 0. The result can be improved to within (√2+1) of the optimum in a special case. In addition, we use numerical experiments to evaluate the proposed consolidation algorithm and observe 30% server reduction compared to several benchmark algorithms.