VirtualRank: A prediction based load balancing technique in virtual computing environment

This paper presents VirtualRank, a load balancing technique which is on the basis of virtual machine migration. VirtualRank proposes a solution that determines when to migrate virtual machines, and where to migrate. Most of the traditional load balancing techniques are based on threshold, whereas VirtualRank predicts load tendency in the upcoming time slots. It ensures a small transient spike which does not trigger needless virtual machine(VM) migration. After triggering migration, the technique selects the potential migration target applying the Markov stochastic process. Finally the weighted probability method is applied to confirm the final migration target. It resolves the accumulation conflicts, as well as increases the stability. We implement our techniques in virtual computing environment iVic and conduct a detailed evaluation using a mix of CPU, network applications. We demonstrate that in different scale virtual network, VirtualRank achieves better load balancing performance, compared with traditional methods.