TY - JOUR
T1 - Lightweight power monitoring framework for virtualized computing environments
AU - Phung, James
AU - Lee, Young Choon
AU - Zomaya, Albert Y.
PY - 2020/1
Y1 - 2020/1
N2 - The pervasive use of virtualization techniques in today's datacenters poses challenges in power monitoring since it is not possible to directly measure the power consumption of a virtual entity such as a virtual machine (VM) and a container. In this paper, we present cWatts++, a lightweight virtual power meter that enables accurate power usage measurement in virtualized computing environments such as VMs and containers of Cloud data centers. At the core of cWatts++ is its application-agnostic power model. To this end, we devise two power models (eventModel and raplModel) that are driven by CPU event counters and the Running Average Power Limit (RAPL) feature of modern Intel CPUs, respectively. While eventModel is more generic and, thus, applicable to a wide range of workloads, raplModel is particularly good for CPU-bound workloads. We have evaluated cWatts++ with its two power models in a real system using the PARSEC benchmark suite and our in-house benchmarks. Our evaluation study demonstrates that these power models have an average error of 4.55 and 1.25 percent, respectively, compared with actual power usage measurements of a real power meter, Cabac Power-Mate.
AB - The pervasive use of virtualization techniques in today's datacenters poses challenges in power monitoring since it is not possible to directly measure the power consumption of a virtual entity such as a virtual machine (VM) and a container. In this paper, we present cWatts++, a lightweight virtual power meter that enables accurate power usage measurement in virtualized computing environments such as VMs and containers of Cloud data centers. At the core of cWatts++ is its application-agnostic power model. To this end, we devise two power models (eventModel and raplModel) that are driven by CPU event counters and the Running Average Power Limit (RAPL) feature of modern Intel CPUs, respectively. While eventModel is more generic and, thus, applicable to a wide range of workloads, raplModel is particularly good for CPU-bound workloads. We have evaluated cWatts++ with its two power models in a real system using the PARSEC benchmark suite and our in-house benchmarks. Our evaluation study demonstrates that these power models have an average error of 4.55 and 1.25 percent, respectively, compared with actual power usage measurements of a real power meter, Cabac Power-Mate.
KW - containers
KW - Energy efficiency
KW - power model
KW - power monitoring
KW - running average power limit
KW - virtualization
UR - http://www.scopus.com/inward/record.url?scp=85078697033&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DP190103710
U2 - 10.1109/TC.2019.2936018
DO - 10.1109/TC.2019.2936018
M3 - Article
AN - SCOPUS:85078697033
SN - 0018-9340
VL - 69
SP - 14
EP - 25
JO - IEEE Transactions on Computers
JF - IEEE Transactions on Computers
IS - 1
ER -