| Title |
Container Power Consumption Prediction Based on GBRT-PL for Edge Servers in Smart City |
| ID_Doc |
37589 |
| Authors |
Ou, DY; Jiang, CF; Zheng, ML; Ren, YJ |
| Title |
Container Power Consumption Prediction Based on GBRT-PL for Edge Servers in Smart City |
| Year |
2023 |
| Published |
Ieee Internet Of Things Journal, 10.0, 21 |
| DOI |
10.1109/JIOT.2023.3281368 |
| Abstract |
Edge computing and Internet of Things (IoT) devices have been widely deployed in smart city applications due to the rapid promotion and implementation of 5G communication technology. Limited by power supply and hardware computing capability, applications on edge servers are mostly deployed and run in the form of container microservices to improve resource utilization. Therefore, identifying the power consumption at the container granularity is of great importance for the load scheduling and service quality assurance of edge servers. In this article, a gradient boosting piecewise linear regression tree (GBRT-PL)-based container power prediction method is proposed. Performance metrics with a strong correlation between container and server power consumption are selected for power consumption modeling. To effectively suit the nonlinear relationship between container performance metrics and server power consumption, the integrated predictive capabilities of multiple regression trees (RTs) and the segmented linear model of single RT leaf nodes are applied. The data from the experiments prove that the GBRT-PL model predicts power consumption more accurately than other models for single and multiple container groups. The highest average relative error rate in the four multicontainer group tests is 6.72%, whereas the highest relative error rate in the 90% quantile is 11.66%. In addition, it can accurately predict the majority of power consumption peaks, which contributes to the precise detection of power consumption anomalies. |
| Author Keywords |
Containers; Power demand; Servers; Cloud computing; Predictive models; Data models; Virtual machining; Container; edge computing; power consumption prediction; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001098109800035 |
| WoS Category |
Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Computer Science; Engineering; Telecommunications |
| PDF |
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