Energy efficiency comparison of hypervisors

Abstract

Current cloud data centers are fully virtualized for service consolidation and power/energy reduction. Although virtualization could reduce the real-time power consumption and overall energy consumption, the energy characteristics of hypervisors hosting different workloads have not been well profiled or understood thus far. In this study, we investigate the power and energy characteristics of four mainstream hypervisors and a container engine, namely VMware ESXi, Microsoft Hyper-V, KVM, XenServer, and Docker, on six different platforms (three mainstream 2U rack servers, one emerging ARM64 server, one desktop server, and one laptop) with power measurements made over prolonged periods. We use computation-intensive, memory-intensive, and mixed Web server-database workloads to explore the power and energy characteristics of different hypervisors in order to emulate realistic multi-tenant cloud environments. The results of extensive experiments conducted with four workload levels (very light, light, fair, and very heavy) indicate that the hypervisors exhibit different power and energy characteristics. Our findings are as follows. (1) Hypervisors exhibit different power and energy consumptions on the same hardware running the same workload. (2) Although mainstream hypervisors have different energy efficiencies aligned with different workload types and workload levels, no single hypervisor outperforms the other hypervisors on all platforms in terms of power or energy consumption. (3) Although container virtualization is considered as lightweight virtualization in terms of implementation and maintenance, it is essentially not more power-efficient than conventional virtualization technology. (4) Although the ARM64 server has low power consumption, it completes computation tasks with a long execution time and, sometimes, high energy consumption. Further, ARM64 servers have medium energy consumption per database operation for mixed workloads. The results presented in this paper can provide system designers and data center operators with useful insights for power-aware workload placement and virtual machine scheduling.