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carlnues@buffalo.edu 2023-08-26 00:07:26 -04:00
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sections/related.tex
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@ -52,31 +52,16 @@ The Maestro system \cite{8410428}, like ours, recognizes that existing policies
Their system focuses on reducing thermal throttling inefficiencies this produces by damping this overperformance.
This system also includes cloud latency along with display quality in its constraint metric.
A different approach by Bui et al. \cite{10.1145/2789168.2790103} saves energy by running loads on phones' little CPUs
Rao et al. acknowledge the need for going beyond a blind general-purpose governor, and tuning performance to particular apps.\cite{rao2017application}
While the common approach to energy reduction cost measurement is to focus on framerate, there are others.
Zhisheng et al. \cite{10.1145/2973750.2973780} constrain streaming, analyzing their system in terms of underlying video quality.
%% HERE...
Begem et al. take the opposite of the general approach and maximize performance pursuant to energy constraints on phones.\cite{7314145}
A system that potentially constrains computation resources needs to measure the cost.
Meeting query latencies or screendraws are common measurements used in previous studies.
None of these, to our knowledge, uses our approach of observing that an approximate energy-minimum setting already suffices to maintain acceptable performance targets, baring specific identifiable cases.
%One, by Kwok et al. \cite{7091048} -- no; latency over minutes (i.e. processed for later consumption)
Rao et al. acknowledge the need for going beyond a blind general-purpose governor, and tuning performance to particular apps.\cite{rao2017application}
They do not...