Date of Award
Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science
Matthew J. Rutherford, Ph.D.
Rinku Dewri, Ph.D.
Energy aware software, Green data structures, Green software, Machine learning, Software adaptation
Dynamic data structures in software applications have been shown to have a large impact on system performance. In this paper, we explore energy saving opportunities of interface-based dynamic data structures. Our results suggest that savings opportunities exist in the C5 Collection between 16.95% and 97.50%. We propose a prototype and architecture for creating adaptive green data structures by applying machine learning tools to build a model for predicting energy efficient data structures based on the dynamic workload. Our neural network model can classify energy efficient data structures based on features such as the number of elements, frequency of operations, interface and set/bag semantics. The 10-fold cross validation result show 95.80% average accuracy of these predictions. Our n-gram model can accurately predict the most energy efficient data structure sequence in 19 simulated and real-world programs - on average, with more than 50% accuracy and up to 98% using a bigram predictor. Our GreenC5 prototype demonstrates how a green data structure can be implemented. With a simple decision making technique, the data structure can efficiently adapt for energy efficiency with low overhead. The median of GreenC5's potential energy savings is more than 60% and ranges from 18% to 95%.
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Received from ProQuest
Michanan, Junya, "GreenC5: An Adaptive, Energy-Aware Collection for Green Software Development" (2016). Electronic Theses and Dissertations. 1122.