A green framework for DBMS based on energy-aware query optimization and energy-efficient query processing. (15th April 2017)

• Record Type: Journal Article
• Authors: Guo, Binglei; Yu, Jiong; Liao, Bin; Yang, Dexian; Lu, Liang
• Is Part Of: Journal of network and computer applications. Volume 84(2017)
• Page Start: 118
• Page End: 130
• Publication Date: 2017-04-15
• Subjects: Green database -- Query optimization -- Energy saving -- Energy efficiency -- Query-plan evaluation -- Energy-aware; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2017.02.015
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Traditional database systems result in high energy consumption and low energy efficiency due to the lack of consideration of energy issues and environmental adaptation in the design process. In this study, we report our recent efforts on this issue, with a focus on energy-aware query optimization and energy-efficient query processing. Firstly, a method of modeling energy cost of query plans during query processing based on their resource consumption patterns is proposed, which helps predict energy cost of queries before execution. Secondly, as the traditional query optimizer focuses on solely optimizing for performance and ignores energy-efficient query plans, a query-plan evaluation model is proposed after a comprehensive study of plan evaluation principles. Using the cost model as a basis, the evaluation model can utilizes the trade-offs between power and performance of plans, and helps the query optimizer select plans that meet performance requirements but result in lower energy cost. Finally, a green database framework integrated with the two above models is proposed to enhance a commercial DBMS. Experimental results reveal that, with reliable and accurate statistical data, the proposed framework in this study can achieve significant energy savings and improve energy efficiency. Highlights: Impact of cache structures on various costs of query processing should be studied. The proposed energy cost model can make an accurate prediction of energy cost. The query-plan evaluation model can help the optimizer select energy-efficient plans.