Modelling energy efficiency for computation

In the last decade, efficient use of energy has become a topic of global significance, touching almost every area of modern life, including computing. From mobile to desktop to server, energy efficiency concerns are now ubiquitous. However, approaches to the energy problem are often piecemeal and focus on only one area for improvement. I argue that the strands of the energy problem are inextricably entangled and cannot be solved in isolation. I offer a high-level view of the problem and, building from it, explore a selection of subproblems within the field. I approach these with various levels of formality, and demonstrate techniques to make improvements on all levels. The original contributions are as follows. Chapter 3 frames the energy problem as one of optimisation with constraints, and explores the impact of this perspective for current commodity products. This includes considerations of the hardware, software and operating system. I summarise the current situation in these respects and propose directions in which they could be improved to better support energy management. Chapter 4 presents mathematical techniques to compute energy-optimal schedules for long-running computations. This work reflects the server-domain concern with energy cost, producing schedules that exploit fluctuations in power cost over time to minimise expenditure rather than raw energy. This assumes certain idealised models of power, performance, cost, and workload, and draws precise formal conclusions from them. Chapter 5 considers techniques to implement energy-efficient real-time streaming. Two classes of problem are considered: first, hard real-time streaming with fixed, predictable frame characteristics; second, soft real-time streaming with a quality-of-service guarantee and probabilistic descriptions of per-frame workload. Efficient algorithms are developed for scheduling frame execution in an energy-efficient way while still guaranteeing hard real-time deadlines. These schedules determine appropriate values for power-relevant parameters , such as dynamic voltage–frequency scaling. A key challenge for future work will be unifying these diverse approaches into one " Theory of Energy " for computing. The progress towards this is summarised in Chapter 6. The thesis concludes by sketching future work towards this Theory of Energy. 3 Acknowledgements First I would like to thank my supervisor Prof. Alan Mycroft, whose faith in my ability to complete this work always exceeded my own. I hope I have repaid the trust and freedom he has granted me over the last three and a half years. I must also thank my family, who unknowingly put me on the road to this PhD …