Performance Overhead among Three Hypervisors: An Experimental Study Using Hadoop Benchmarks

Abstract

Hyper visors are widely used in cloud environments and their impact on application performance has been a topic of significant research and practical interest. We conducted experimental measurements of several benchmarks using Hadoop MapReduce to evaluate and compare the performance impact of three popular hyper visors: a commercial hyper visor, Xen, and KVM. We found that differences in the workload type (CPU or I/O intensive), workload size and VM placement yielded significant performance differences among the hyper visors. In our study, we used the three hyper visors to run several MapReduce benchmarks, such as Word Count, TestDSFIO, and TeraSort and further validated our observed hypothesis using micro benchmarks. In our observation for CPU-bound benchmark, the performance difference between the three hyper visors was negligible, however, significant performance variations were seen for I/O-bound benchmarks. Moreover, adding more virtual machines on the same physical host degraded the performance on all three hyper visors, yet we observed different degradation trends amongst them. Concretely, the commercial hyper visor is 46% faster at TestDFSIO Write than KVM, but 49% slower in the TeraSort benchmark. In addition, increasing the workload size for TeraSort yielded completion times for CVM that were two times that of Xen and KVM. The performance differences shown between the hyper visors suggests that further analysis and consideration of hyper visors are needed in the future when deploying applications to cloud environments.