Abstract
Energy-efficient scheduling algorithms based on multiple Directed Acyclic Graph( DAG) fail to save energy efficiently, have a narrow application scope and cannot take performance optimization into account. In order to solve these problems, Multiple Relation Energy Optimizing( MREO) was proposed for multiple DAG workflows. MREO integrated independent tasks to reduce the number of processors used, on the basis of analyzing the characteristics of computationintensive and communication-intensive tasks. Backtracking and branch-and-bound algorithm were employed to select the best integration path dynamically and reduce the complexity of the algorithm at the same time. The experimental results demonstrate that MREO can reduce the computation and communication energy cost efficiently and get a good energy saving effect on the premise of guaranteeing the performance of multiple DAG workflows.
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