{"title":"Energy efficiency comparison of hypervisors","authors":"Congfeng Jiang, Dongyang Ou, Yumei Wang, Xindong You, Jilin Zhang, Jian Wan, Bing Luo, Weisong Shi","doi":"10.1109/IGCC.2016.7892607","DOIUrl":null,"url":null,"abstract":"Current cloud data centers are fully virtualized for service consolidations and power/energy reduction. Although virtualization could reduce real time power and overall energy consumption, the energy characteristics of hypervisors hosting different workloads are not well profiled and understood. In this paper, we investigate the power and energy characteristics of mainstream hypervisors and container engine, i.e., VMware ESXi, Microsoft Hyper-V, KVM, XenServer and Docker, on five different platforms (two mainstream 2U rack servers, one emerging ARM64 server, one desktop server, and one laptop) with hundreds of hours' power measures. We use both computing intensive and mixed web server-database workloads to explore the power and energy characteristics of different hypervisors. Extensive experiment results of four workload levels (very light, light, fair, and very heavy workload) demonstrate that hypervisors expose different power and energy characteristics. We find that: (1) Hypervisors expose different power and energy consumption on the same hardware running same workloads. (2) Although mainstream hypervisors have different energy efficiencies aligned with different workload types and workload levels, there is no single hypervisor that outperforms all other hypervisors on all platforms in terms of power or energy consumptions. (3) Although container virtualization is considered as light-weight virtualization in terms of implementation and maintenance, it is not essentially more power efficient than traditional virtualization technology. (4) ARM64 server does have lower power consumption, but they finish computing jobs with longer execution time and sometimes consume more energy. And ARM64 servers has medium energy consumption per database operations for mixed workloads. The results presented in this paper provide useful insights to system designers, as well as data center operators for power-aware workload placement and virtual machine scheduling.","PeriodicalId":171308,"journal":{"name":"2016 Seventh International Green and Sustainable Computing Conference (IGSC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"48","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Seventh International Green and Sustainable Computing Conference (IGSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IGCC.2016.7892607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 48
Abstract
Current cloud data centers are fully virtualized for service consolidations and power/energy reduction. Although virtualization could reduce real time power and overall energy consumption, the energy characteristics of hypervisors hosting different workloads are not well profiled and understood. In this paper, we investigate the power and energy characteristics of mainstream hypervisors and container engine, i.e., VMware ESXi, Microsoft Hyper-V, KVM, XenServer and Docker, on five different platforms (two mainstream 2U rack servers, one emerging ARM64 server, one desktop server, and one laptop) with hundreds of hours' power measures. We use both computing intensive and mixed web server-database workloads to explore the power and energy characteristics of different hypervisors. Extensive experiment results of four workload levels (very light, light, fair, and very heavy workload) demonstrate that hypervisors expose different power and energy characteristics. We find that: (1) Hypervisors expose different power and energy consumption on the same hardware running same workloads. (2) Although mainstream hypervisors have different energy efficiencies aligned with different workload types and workload levels, there is no single hypervisor that outperforms all other hypervisors on all platforms in terms of power or energy consumptions. (3) Although container virtualization is considered as light-weight virtualization in terms of implementation and maintenance, it is not essentially more power efficient than traditional virtualization technology. (4) ARM64 server does have lower power consumption, but they finish computing jobs with longer execution time and sometimes consume more energy. And ARM64 servers has medium energy consumption per database operations for mixed workloads. The results presented in this paper provide useful insights to system designers, as well as data center operators for power-aware workload placement and virtual machine scheduling.