Computer Science ›› 2017, Vol. 44 ›› Issue (1): 208-213.doi: 10.11896/j.issn.1002-137X.2017.01.040

Previous Articles     Next Articles

SQL Energy Consumption Forecasting Model Based on Database Load Status

GUO Bing-lei, YU Jiong, LIAO Bin and YANG De-xian   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In a typical database server,performance (throughput or response time) is the first-class optimization goal.However,energy consumption of database systems is ignored by the service providers and users,which makes the high energy consumption be a serious problem in data centers in the processing of chasing performance.Building energy consumption model for query workload is the first step to create a green database.By quantifying the system resources (CPU and Disk) consumed by query workload and transforming the time cost and energy cost into two independent models (time estimation model and power estimation model),the energy estimation model with uniform resource unit was implemented in a single-site database server.Using the multiple linear regression method to compute the key parameters of the above models,experimental results prove the feasibility of our model.To further prove the accuracy and efficiency of the above model,we also made it work under two different system settings (static system and dynamic system),making it more suitable for building the energy-aware green database.

Key words: Green computing,SQL execution energy consumption,Query processing,Green database

[1] POESS M,NAMBIAR R O.Energy cost,the key challenge oftoday’s data centers:a power consumption analysis of TPC-C results[J].Proceedings of the VLDB Endowment,2008,1(2):1229-1240.
[2] KURP P.Green Computing[J].Communications of the ACM,2008,51(10):11-13.
[3] HARIZOPOULOS S,SHAH M,MEZA J,et al.Energy efficiency:The new holy grail of data management systems research[J].arXiv preprint arXiv:0909.1784,2009.
[4] BRILL K G.Data center energy efficiency and productivity[J].the Uptime Insititute-White Paper,2007(5):176-184.
[5] LIAO Bin,YU Jiong,ZHANG Tao,et al.Energy-Efficient Algorithms for Distributed File System HDFS[J].Chinese Journal of Computers,2013,36(5):1047-1064.(in Chinese) 廖彬,于炯,张陶,等.基于分布式文件系统HDFS的节能算法研究[J].计算机学报,2013,36(5):1047-1064.
[6] LIAO Bin,YU Jiong,SHUN Hua,et al.Energy-Efficient Algorithms for Distributed Storage System Based on Data Storage Structure Reconfiguration [J].Journal of Computer Research and Development,2013,50(1):3-18.(in Chinese) 廖彬,于炯,孙华,等.基于存储结构重配置的分布式存储系统节能算法[J].计算机研究与发展,2013,50(1):3-18.
[7] LIAO Bin,YU Jiong,ZHANG Tao,et al.Novel Energy-efficient Metadata Dynamic Modeling and Management Approach for Cloud Storage System[J].Journal of Chinese Computer Systems,2013,10(34):2407-2412.(in Chinese) 廖彬,于炯,张陶,等.一种适应节能的云存储系统元数据动态建模与管理方法[J].小型微型计算机系统,2013,10(34):2407-2412.
[8] GRAY J.Tape is dead,disk is tape,flash is disk,RAM locality isking.http://signallate.com/signallake.com/innovation/Flash_is_Good.pdf.
[9] WANG Jiang-tao,LAI Wen-yu,MENG Xiao-feng.Flash-Based Database:Studies,Techniques and Forecasts [J].Chinese Journal of Computers,2013,36(8):1549-1567.(in Chinese) 王江涛,赖文豫,孟小峰.闪存数据库:现状,技术与展望[J].计算机学报,2013,36(8):1549-1567 .
[10] RODRIGUEZ-MARTINEZ M,VALDIVIA H,SEGUEL J,et al.Estimating Power/Energy consumption in Database Servers[J].Procedia Computer Science,2011,6(1):112-117.
[11] XU Z.Building a power-aware database management system[C]∥Proceedings of the Fourth SIGMOD PhD Workshop on Innovative Database Research.ACM,2010:1-6.
[12] XU Z,TU Y C,WANG X.Exploring power-performance tra-deoffs in database systems[C]∥2010 IEEE 26th International Conference on Data Engineering (ICDE).IEEE,2010:485-496.
[13] XU Z,TU Y C,WANG X.PET:reducing database energy cost via query optimization[J].Proceedings of the VLDB Endowment,2012,5(12):1954-1957.
[14] TSIROGIANNIS D,HARIZOPOULOS S,S HAH M A.Analyzing the energy efficiency of a database server[C]∥Proc.of SIGMOD ’10 Indianapolis.IN,USA,2010:231-242.
[15] JIN Pei-quan,XING Bao-ping,JIN Yong,et al.Survey on energy-aware green databases[J].Journal of Computer Application,2014,34(1):46-53.(in Chinese) 金培权,邢宝平,金勇,等.能耗感知的绿色数据库研究综述[J].计算机应用,2014,34(1):46-53.
[16] YANG Liang-huai,ZHU Hong-yan.Whole system realtimepower profiling and modeling[J].Journal of Computer Science,2014,41(9):32-37.(in Chinese) 杨良怀,朱红燕.整机系统实时功率剖析与建模[J].计算机科学,2014,41(9):32-37.
[17] GUO Bing-lei,YU Jiong,LIAO Bin,et al.SQL Energy Con-sumption Modeling and Optimization Research[J].Journal of Computer Science,2015,2(10):202-207.(in Chinese) 国冰磊,于炯,廖彬,等.SQL能耗建模及优化研究[J].计算机科学,2015,2(10):202-207.
[18] LANG W,PATEL J.Towards eco-friendly database manage-ment systems[J].arXiv preprint arXiv:0909.1767,2009.
[19] ZHU Yi,XIAO Fang-xiong,ZHOU Hang,et al.Method forModeling and Analyzing Software Energy Consumption of Embedded Real-Time System[J].Journal of Computer Reserach and Development,2014,51(4):848-855.(in Chinese) 祝义,肖芳雄,周航,等.一种嵌入式实时系统软件能耗建模与分析的方法[J].计算机研究与发展,2015,51(4):848-855.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .