Impact of virtual machine granularity on cloud computing workloads performance

Abstract

This paper studies the impact of VM granularity on workload performance in cloud computing environments. We use HPL as a representative tightly coupled computational workload and a web server providing content to customers as a representative loosely coupled network intensive workload. The performance evaluation demonstrates VM granularity has a significant impact on the performance of the computational workload. On an 8-CPU machine, the performance obtained from utilizing 8VMs is more than 4 times higher than that given by 4 or 16 VMs for HPL of problem size 4096; whereas on two machines with a total of 12 CPUs 24 VMs gives the best performance for HPL of problem sizes from 256 to 1024. Our results also indicate that the effect of VM granularity on the performance of the web system is not critical. The largest standard deviation of the transaction rates obtained from varying VM granularity is merely 2.89 with a mean value of 21.34. These observations suggest that VM malleability strategies where VM granularity is changed dynamically, can be used to improve the performance of tightly coupled computational workloads, whereas VM consolidation for energy savings can be more effectively applied to loosely coupled network intensive workloads.