DVMP: Incremental traffic-aware VM placement on heterogeneous servers in data centers

Abstract

As the tremendous momentum cloud computing has grown, the modern data center networks are facing challenge to handle the increasing traffic demand among virtual machines (VMs). Simply adding more switches and links may increase network capacity but at the same time increase the complexity and infrastructure cost. Thus, intelligent VM placement has been proposed to reduce the intra-DC traffic. Prior solutions model the traffic-aware VM placement problem as a Balanced Minimum K-cut Problem (BMKP). However, the assumptions of “once-for-all” VM placement on physical servers with equal VM slots are often not realistic in practical data centers, and thus the naive BMKP model may lead to suboptimal placement solutions. In this work, we revisit the problem by considering the server heterogeneity and propose an incremental traffic-aware VM placement algorithm. Given that the BMKP model cannot be directly applied, we make a number of transformations to re-establish the model. First, by introducing pseudo VM slots on physical servers with less VM slots, we allow the number of available VM slots of each server to be different. Second, pseudo edges with infinite costs are added between existing VMs, and thus previously deployed VMs on the same physical server will still be packed together. Third, a change on the number of pseudo VM slots is applied, so that existing VMs placed on different physical servers will still be separated. In this way, we reduce the problem to a new BMKP problem, which results in a much better solution. The evaluation results show that DVMP can reduce up to 28%, 39% and 55% traffic compared with naive BMKP model, greedy VM placement and random VM placement, respectively.