Applying Self-powered Sensor And Support Vector Machine In Load Energy Consumption Modeling And Prediction Of Relational Database

This paper explores the analysis and modeling of energy consumption in the context of database workloads, leveraging vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM). Experiments validate the performance of self-sustaining WVS in providing power and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Additionally, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%.