Dobson, Richard Mark

English

Smartphones are potent mobile devices which are required to operate for extendedperiods of time on battery power. In this thesis smartphone powermanagement issues are addressed using algorithmic techniques.Firstly, we consider power efficient scheduling for heterogeneous multi processorsystems that allow dynamic speed scaling. We propose the Virtual SingleProcessor (VSP) approach which involves computing and utilising optimal systemconfigurations. The VSP is used in combination with an efficient singleprocessor dynamic speed scaling scheduling algorithm to compute highly powerefficient schedules. We find that there is an average power saving of between4.4% (2 processor system) and 8.175% (16 processor system) when comparedto an alternative algorithm. Simulations also showed that the VSP approachreduced the objective function ofPWeighted Flow + Energy by 2.31% morethan the best known alternative. This work was published as a full paper atMISTA 2011.Secondly, we discuss low energy Field Programmable Gate Array (FPGA)function mapping. A substantial FPGA power drain is caused by dynamicswitching of the routing edges; this can be vastly reduced by mapping the inputboolean function such that switching activity is minimised. We formulate thecombinatorial optimisation problem, develop a complete neighbourhood functionand apply simulated annealing to minimise cumulative switching. We findthat our algorithm reduces the cumulative switching activity by an average of27.44% compared to a genetic algorithm. This work appeared at GreenGEC2013.Finally, we examine the sleep state management problem in terms of advicecomplexity. We begin by showing the advice complexity of the problem is r log swhere r is the number of idle periods and s is the number of sleep states. Wedesign an algorithm which uses a single bit of advice to solve the single sleepstate problem and show it to be 1.8-competitve. This is 20% better than thebest possible deterministic algorithm. We also show that our algorithm canbe improved by adding more advice but only until we have [log b] advice bits.Finally, in the case with more than 2 states our algorithm uses 1 bit of adviceto improve on the deterministic algorithm

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Algorithms and Models for Optimal Power Management on Smartphones

https://kclpure.kcl.ac.uk/portal/files/51899990/2014_Dobson_Richard_0950190_ethesis.pdf

Algorithms and Models for Optimal Power Management on Smartphones

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