![]() ![]() ![]() 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. Finally, a green database framework integrated with the two above models is proposed to enhance a commercial DBMS. 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. 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. there are costs because of operations like projection, selection, join etc.DBMS strives to process the query in the most efficient way (in terms of ‘Time’) to produce the answer.In this paper we proposed a novel method for query optimization using heuristic based approach. 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. In this study, we report our recent efforts on this issue, with a focus on energy-aware query optimization and energy-efficient query processing. 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. ![]()
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