{"title":"用于时间敏感应用的自适应K8S云控制器","authors":"L. Bulej, T. Bures, P. Hnetynka, Danylo Khalyeyev","doi":"10.1109/SEAA53835.2021.00029","DOIUrl":null,"url":null,"abstract":"The paper presents a self-adaptive Kubernetes cloud controller for scheduling time-sensitive applications. The controller allows services to specify timing requirements (response time or throughput) and schedules services on shared cloud resources so as to meet the requirements. The controller builds and continuously updates an internal performance model of each service and uses it to determine the kind of resources needed by a service, as well as predict potential contention on shared resources, and (re-)deploys services accordingly. The controller is integrated with our highly-customizable data processing and visualization platform IVIS, which provides a web-based front-end for service deployment and visualization of results. The controller implementation is open-source and is intended to provide an easy-to-use testbed for experiments focusing on various aspects of adaptive scheduling and deployment in the cloud.","PeriodicalId":435977,"journal":{"name":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"2 4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Self-adaptive K8S Cloud Controller for Time-sensitive Applications\",\"authors\":\"L. Bulej, T. Bures, P. Hnetynka, Danylo Khalyeyev\",\"doi\":\"10.1109/SEAA53835.2021.00029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents a self-adaptive Kubernetes cloud controller for scheduling time-sensitive applications. The controller allows services to specify timing requirements (response time or throughput) and schedules services on shared cloud resources so as to meet the requirements. The controller builds and continuously updates an internal performance model of each service and uses it to determine the kind of resources needed by a service, as well as predict potential contention on shared resources, and (re-)deploys services accordingly. The controller is integrated with our highly-customizable data processing and visualization platform IVIS, which provides a web-based front-end for service deployment and visualization of results. The controller implementation is open-source and is intended to provide an easy-to-use testbed for experiments focusing on various aspects of adaptive scheduling and deployment in the cloud.\",\"PeriodicalId\":435977,\"journal\":{\"name\":\"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)\",\"volume\":\"2 4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SEAA53835.2021.00029\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SEAA53835.2021.00029","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Self-adaptive K8S Cloud Controller for Time-sensitive Applications
The paper presents a self-adaptive Kubernetes cloud controller for scheduling time-sensitive applications. The controller allows services to specify timing requirements (response time or throughput) and schedules services on shared cloud resources so as to meet the requirements. The controller builds and continuously updates an internal performance model of each service and uses it to determine the kind of resources needed by a service, as well as predict potential contention on shared resources, and (re-)deploys services accordingly. The controller is integrated with our highly-customizable data processing and visualization platform IVIS, which provides a web-based front-end for service deployment and visualization of results. The controller implementation is open-source and is intended to provide an easy-to-use testbed for experiments focusing on various aspects of adaptive scheduling and deployment in the cloud.