在负载能量消耗建模和关系数据库预测中应用自动供电的传感器和支向量机器
Dexian Yang1, Jiong Yu2, Zhenzhen He2
1School of Information Science and Engineering, Xinjiang University, Ürümqi, 830046, China. yangdexian@stu.xju.edu.cn.
Scientific reports
|November 5, 2023
概括
这项研究使用振动能量采集和机器学习算法对数据库能源消耗进行了建模. 开发的模型准确地预测了环保数据库管理系统的能源使用情况.
科学领域:
- 计算机科学 计算机科学
- 可持续的计算 可持续的计算
- 数据库系统 数据库系统
背景情况:
- 数据库管理系统 (DBMS) 是重要的能源消费者.
- 开发节能DBMS对于可持续性至关重要.
- 现有的能源消耗模型对于不同的工作负载缺乏准确性.
研究的目的:
- 为关系数据库工作负载开发精确的能源消耗模型 (ECM).
- 为了实现创建环保的DBMS.
- 为了验证振动能量收集用于为传感器供电的使用.
主要方法:
- 使用自助式无线振动传感器 (WVS) 供电.
- 采用最小正方形支向量机器算法.
- 在各种SQL操作下开发和评估关系数据库工作负载的ECM.
主要成果:
- 对于数据库的能源消耗,ECM的预测准确度很高,查询操作的错误率低至1-4%.
- 该模型在执行SQL语句时实现了10%的最大预测错误率.
- 与替代模型相比,ECM显示出更高的精度,特别是在单块读取和顺序读取操作方面.
结论:
- 拟议的ECM提供了一种可靠的方法来预测关系数据库中的能源消耗.
- 这项研究为开发可持续,环保的DBMS提供了理论基础和实际指导.
- 振动能量采集是WVS在数据库监控应用中的可行电源.
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