{"title":"时空、热和能量感知调度的通用学习多智能体系统方法","authors":"Christina Herzog, J. Pierson","doi":"10.1109/PDP2018.2018.00010","DOIUrl":null,"url":null,"abstract":"This paper proposes an agent based approach to the scheduling of jobs in data centers under thermal constraints. The model encompasses both temporal and spatial aspects of the temperature evolution using a unified model, taking into account the dynamics of heat production and dissipation. Agents coordinate to eventually move jobs to the best suitable place and to adapt dynamically the frequency settings of the nodes to the best combination. Several objectives of the agents are compared under different circumstances by an extensive set of experiments.","PeriodicalId":333367,"journal":{"name":"2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Generic Learning Multi-agent-System Approach for Spatio-Temporal-, Thermal- and Energy-Aware Scheduling\",\"authors\":\"Christina Herzog, J. Pierson\",\"doi\":\"10.1109/PDP2018.2018.00010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an agent based approach to the scheduling of jobs in data centers under thermal constraints. The model encompasses both temporal and spatial aspects of the temperature evolution using a unified model, taking into account the dynamics of heat production and dissipation. Agents coordinate to eventually move jobs to the best suitable place and to adapt dynamically the frequency settings of the nodes to the best combination. Several objectives of the agents are compared under different circumstances by an extensive set of experiments.\",\"PeriodicalId\":333367,\"journal\":{\"name\":\"2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PDP2018.2018.00010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PDP2018.2018.00010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Generic Learning Multi-agent-System Approach for Spatio-Temporal-, Thermal- and Energy-Aware Scheduling
This paper proposes an agent based approach to the scheduling of jobs in data centers under thermal constraints. The model encompasses both temporal and spatial aspects of the temperature evolution using a unified model, taking into account the dynamics of heat production and dissipation. Agents coordinate to eventually move jobs to the best suitable place and to adapt dynamically the frequency settings of the nodes to the best combination. Several objectives of the agents are compared under different circumstances by an extensive set of experiments.