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Title: Algorithms and models for optimal power management on smartphones
Author: Dobson, Richard Mark
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2014
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Smartphones are potent mobile devices which are required to operate for extended periods of time on battery power. In this thesis smartphone power management issues are addressed using algorithmic techniques. Firstly, we consider power efficient scheduling for heterogeneous multi processor systems that allow dynamic speed scaling. We propose the Virtual Single Processor (VSP) approach which involves computing and utilising optimal system configurations. The VSP is used in combination with an efficient single processor dynamic speed scaling scheduling algorithm to compute highly power efficient schedules. We find that there is an average power saving of between 4.4% (2 processor system) and 8.175% (16 processor system) when compared to an alternative algorithm. Simulations also showed that the VSP approach reduced the objective function of P Weighted Flow + Energy by 2.31% more than the best known alternative. This work was published as a full paper at MISTA 2011. Secondly, we discuss low energy Field Programmable Gate Array (FPGA) function mapping. A substantial FPGA power drain is caused by dynamic switching of the routing edges; this can be vastly reduced by mapping the input boolean function such that switching activity is minimised. We formulate the combinatorial optimisation problem, develop a complete neighbourhood function and apply simulated annealing to minimise cumulative switching. We find that our algorithm reduces the cumulative switching activity by an average of 27.44% compared to a genetic algorithm. This work appeared at GreenGEC 2013. Finally, we examine the sleep state management problem in terms of advice complexity. We begin by showing the advice complexity of the problem is r log s where r is the number of idle periods and s is the number of sleep states. We design an algorithm which uses a single bit of advice to solve the single sleep state problem and show it to be 1.8-competitve. This is 20% better than the best possible deterministic algorithm. We also show that our algorithm can be 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 advice to improve on the deterministic algorithm.
Supervisor: Steinhofel, Kathleen Kristine Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available