Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

How can we help software developers address software energy consumption? Software energy consumption threatens the battery life of your smart-phone and the sustainability of data-centers. Computation costs energy, and this fact is not lost on smart-phone users whose batteries run out due to apps. Scaramella et al.[12] of IDC shows that for every $1000 worth of equipment in their data center, $500 more will be spent just to power and cool it over its lifetime. Software energy consumption is a sustainability concern as the energy is sometimes produced by coal powered plants. It also affects the availability of energy for mobile devices. Software developers are responsible, yet research shows that they lack expensive hardware, tools, and training needed to address software energy consumption [J4].

Software energy efficiency depends on the purpose of the software, whether it is office, game, or productivity software. The tasks and expected energy efficiency of email applications are different than video-games or office software. We seek to model this difference in expected efficiency by leveraging a large software performance database created by crowd-sourcing and test generation. By offering developers and open-source projects software analytics---dashboards, analysis, and storage---we can encourage them to contribute their performance data (crowd-sourcing) to build domain specific software energy models. In turn, these models will help developers estimate their applications' energy consumption to help satisfy customers, and to reduce costs.

This research program seeks to help software developers reduce software energy consumption via data collection, empirical analysis, practical guidelines, and theory building. The objectives of this program include:

· Green Data: How can we promote the recording and sharing various kinds of performance data to help software energy research and create specialized energy consumption models?

· Green Programmers: What are the domain specific energy issues that developers face, that we can learn about through interviews and surveys?

· Green Predictions: How can we leverage crowd-sourced performance big data to build relevant and accurate domain-specific models?

· Green Best-Practices: Can we extract best practices for energy efficient software from existing software measurements mined by green crowd-sourcing?

· Green Theory: How can developers improve software energy consumption before they build software? Can we combine algorithmic complexity and data-mining to help programmers design for energy efficiency?

The long term goal is to produce and exploit crowd-sourced performance measurements so we may produce software energy consumption best practices and models. This program will train HQP with skills sought by mobile and data-center oriented companies such as RIM, Microsoft (who hired 2 of my MSc students), Apple, HTC, Intel, and Samsung.