01771nas a2200157   4500008004100000245007400041210006900115260001200184300001300196490000700209520129700216100002201513700002301535700001901558856003601577       2019                        eng d00aAutomated Model-based Optimization of Data-Adaptable Embedded Systems0 aAutomated Modelbased Optimization of DataAdaptable Embedded Syst  c02/2020  a22 pages0 v193 a<p><span style="caret-color: rgb(85, 85, 85); color: rgb(85, 85, 85); font-family: &quot;open sans&quot;, myriad-pro, sans-serif; font-size: 15.161600112915039px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration: none; display: inline !important; float: none;">This paper presents a modeling and optimization framework that enables developers to model an application's data sources, tasks, and exchanged data tokens; specify application requirements through high-level design metrics and fuzzy logic based optimization rules; and define an estimation framework to automatically optimize the application at runtime. We demonstrate the modeling and optimization process via an example application for video-based vehicle tracking and collision avoidance. We analyze the benefits of runtime optimization by comparing the performance of static point solutions to dynamic solutions over five distinct execution scenarios, showing improvements of up to 74% for dynamic over static configurations.</span></p>
1 aLizarraga, Adrian1 aSprinkle, Jonathan1 aLysecky, Roman  uhttps://doi.org/10.1145/3372142