Multi-disciplinary Green IT Archival Analysis: A Pathway for Future Studies

With the growth of information technology (IT), there is a growing global concern about the environmental impact of such technologies. As such, academics in several research disciplines consider research on green IT a vibrant theme. While the disparate knowledge in each discipline is gaining substantial momentum, we need a consolidated multi-disciplinary view of the salient findings of each research discipline for green IT research to reach its full potential. We reviewed 390 papers published on green IT from 2007 to 2015 in three disciplines: computer science, information systems and management. The prevailing literature demonstrates the value of this consolidated approach for advancing our understanding on this complex global issue of environmental sustainability. We provide an overarching theoretical perspective to consolidate multi-disciplinary findings and to encourage information systems researchers to develop an effective cumulative tradition of research

GreenC5: An Adaptive, Energy-Aware Collection for Green Software Development

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\u27s potential energy savings is more than 60% and ranges from 18% to 95%