{"title":"实时嵌入式系统能量感知调度的元启发式性能分析","authors":"Ashraf Suyyagh, J. G. Tong, Z. Zilic","doi":"10.1109/AEECT.2015.7360554","DOIUrl":null,"url":null,"abstract":"Energy efficient real-time systems has been a prime concern in the last few years. Techniques on all levels of system design from the physical up to operating system level are being developed to reduce energy consumption. Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM) are among the most widely used methods. Most research efforts focused on reducing processor power. Recently, system-wide solutions have been investigated. In this work, we extend on the previous work by adapting two evolutionary algorithms for system-wide energy minimisation. We show that our meta-heuristics improve on previous work and are three times more likely to reach near-optimal energy savings.","PeriodicalId":227019,"journal":{"name":"2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Analysis of meta-heuristics performance in energy aware scheduling of real-time embedded systems\",\"authors\":\"Ashraf Suyyagh, J. G. Tong, Z. Zilic\",\"doi\":\"10.1109/AEECT.2015.7360554\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Energy efficient real-time systems has been a prime concern in the last few years. Techniques on all levels of system design from the physical up to operating system level are being developed to reduce energy consumption. Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM) are among the most widely used methods. Most research efforts focused on reducing processor power. Recently, system-wide solutions have been investigated. In this work, we extend on the previous work by adapting two evolutionary algorithms for system-wide energy minimisation. We show that our meta-heuristics improve on previous work and are three times more likely to reach near-optimal energy savings.\",\"PeriodicalId\":227019,\"journal\":{\"name\":\"2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEECT.2015.7360554\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEECT.2015.7360554","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of meta-heuristics performance in energy aware scheduling of real-time embedded systems
Energy efficient real-time systems has been a prime concern in the last few years. Techniques on all levels of system design from the physical up to operating system level are being developed to reduce energy consumption. Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management (DPM) are among the most widely used methods. Most research efforts focused on reducing processor power. Recently, system-wide solutions have been investigated. In this work, we extend on the previous work by adapting two evolutionary algorithms for system-wide energy minimisation. We show that our meta-heuristics improve on previous work and are three times more likely to reach near-optimal energy savings.