Name
Abstract | ||
|---|---|---|
AbstractAs the increasing IT energy consumption emerged as a prominent issue, computer system energy consumption monitoring and optimization has gradually become a significant research forefront. However, most existing energy monitoring methods are limited to hardware-based measurement or coarse-grained energy consumption estimation. They cannot provide fine-grained energy consumption data i.e., component energy consumption and high-scalability for distributed cloud environments. In this article, the authors first study widely-used power models of CPUs, memory and hard disks. Then, following an investigation into disk power behaviors in sequential I/O and random I/O, they propose an improved I/O-mode aware disk power model with multiple variables and thresholds. They developed EnergyMeter, a monitoring software utility that can provide accurate power estimate by exploiting a multi-component power model. Experiments based on PCMark prove that the average error of EnergyMeter is merely 5% under a variety of workloads |
Year | DOI | Venue |
|---|---|---|
2018 | 10.4018/IJGHPC.2018010102 | Periodicals |
Keywords | Field | DocType |
Cloud Computing, Power Measurement, Power Model, Power Monitoring | Monitoring system,Computer science,Power model,Distributed computing | Journal |
Volume | Issue | ISSN |
10 | 1 | 1938-0259 |
Citations | PageRank | References |
1 | 0.35 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Lin | 1 | 143 | 12.22 |
| Haoyu Wang | 2 | 46 | 13.75 |
| Wentai Wu | 3 | 18 | 1.91 |