Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database.

ABSTRACT: This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages 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) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% dur

SUBMITTER: Yang D 

PROVIDER: S-EPMC10625616 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature