{"title":"EnerQuery: Energy-Aware Query Processing","authors":"Amine Roukh, Ladjel Bellatreche, C. Ordonez","doi":"10.1145/2983323.2983334","DOIUrl":null,"url":null,"abstract":"Energy consumption is increasingly more important in large-scale query processing. This problem requires revisiting traditional query processing in actual DBMSs to identify the potential of energy saving, and to study the trade-offs between energy consumption and performance. In this paper, we propose EnerQuery, a tool built on top of a traditional DBMS to capitalize the efforts invested in building energy-aware query optimizers, which have the lion's share in energy consumption. Energy consumption is estimated on all query plan steps and integrated into a mathematical linear cost model used to select the best query plans. To increase end users' energy awareness, EnerQuery features a diagnostic GUI to visualize energy consumption per step and its savings when tuning key parameters during query execution.","PeriodicalId":250808,"journal":{"name":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 25th ACM International on Conference on Information and Knowledge Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2983323.2983334","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 16
Abstract
Energy consumption is increasingly more important in large-scale query processing. This problem requires revisiting traditional query processing in actual DBMSs to identify the potential of energy saving, and to study the trade-offs between energy consumption and performance. In this paper, we propose EnerQuery, a tool built on top of a traditional DBMS to capitalize the efforts invested in building energy-aware query optimizers, which have the lion's share in energy consumption. Energy consumption is estimated on all query plan steps and integrated into a mathematical linear cost model used to select the best query plans. To increase end users' energy awareness, EnerQuery features a diagnostic GUI to visualize energy consumption per step and its savings when tuning key parameters during query execution.