{"title":"PRESTO:用于云原生微服务的延迟感知功率上限编排器","authors":"Rolando Brondolin, M. Santambrogio","doi":"10.1109/ACSOS49614.2020.00021","DOIUrl":null,"url":null,"abstract":"Power consumption is a major concern for cloud data-centers. In this context, cloud-native applications emerged in the last few years and fostered the adoption of the cloud computing model across many organizations. Cloud-native workloads are highly heterogeneous, co-located and latency-sensitive and are able to scale to a high number of machines. To properly manage their power consumption, within this paper we propose Power REgulator for Service Time Optimization (PRESTO), a latency-aware power-capping orchestrator. PRESTO defines an Observe Decide Act (ODA) loop to manage power consumption and average latency of microservice-based workloads by considering all the network interactions between microservices in the cluster. PRESTO reduces the power consumption by 37.13% on average with a control error that is below 12.5% and below 1.5ms on average w.r.t. an unconstrained execution.","PeriodicalId":310362,"journal":{"name":"2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"PRESTO: a latency-aware power-capping orchestrator for cloud-native microservices\",\"authors\":\"Rolando Brondolin, M. Santambrogio\",\"doi\":\"10.1109/ACSOS49614.2020.00021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Power consumption is a major concern for cloud data-centers. In this context, cloud-native applications emerged in the last few years and fostered the adoption of the cloud computing model across many organizations. Cloud-native workloads are highly heterogeneous, co-located and latency-sensitive and are able to scale to a high number of machines. To properly manage their power consumption, within this paper we propose Power REgulator for Service Time Optimization (PRESTO), a latency-aware power-capping orchestrator. PRESTO defines an Observe Decide Act (ODA) loop to manage power consumption and average latency of microservice-based workloads by considering all the network interactions between microservices in the cluster. PRESTO reduces the power consumption by 37.13% on average with a control error that is below 12.5% and below 1.5ms on average w.r.t. an unconstrained execution.\",\"PeriodicalId\":310362,\"journal\":{\"name\":\"2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)\",\"volume\":\"88 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSOS49614.2020.00021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSOS49614.2020.00021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PRESTO: a latency-aware power-capping orchestrator for cloud-native microservices
Power consumption is a major concern for cloud data-centers. In this context, cloud-native applications emerged in the last few years and fostered the adoption of the cloud computing model across many organizations. Cloud-native workloads are highly heterogeneous, co-located and latency-sensitive and are able to scale to a high number of machines. To properly manage their power consumption, within this paper we propose Power REgulator for Service Time Optimization (PRESTO), a latency-aware power-capping orchestrator. PRESTO defines an Observe Decide Act (ODA) loop to manage power consumption and average latency of microservice-based workloads by considering all the network interactions between microservices in the cluster. PRESTO reduces the power consumption by 37.13% on average with a control error that is below 12.5% and below 1.5ms on average w.r.t. an unconstrained execution.