{"title":"迈向微服务的最优配置","authors":"Gagan Somashekar, Anshul Gandhi","doi":"10.1145/3437984.3458828","DOIUrl":null,"url":null,"abstract":"The microservice architecture allows applications to be designed in a modular format, whereby each microservice can implement a single functionality and can be independently managed and deployed. However, an undesirable side-effect of this modular design is the large state space of possibly inter-dependent configuration parameters (of the constituent microservices) which have to be tuned to improve application performance. This workshop paper investigates optimization techniques and dimensionality reduction strategies for tuning microservices applications, empirically demonstrating the significant tail latency improvements (as much as 23%) that can be achieved with configuration tuning.","PeriodicalId":269840,"journal":{"name":"Proceedings of the 1st Workshop on Machine Learning and Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Towards Optimal Configuration of Microservices\",\"authors\":\"Gagan Somashekar, Anshul Gandhi\",\"doi\":\"10.1145/3437984.3458828\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The microservice architecture allows applications to be designed in a modular format, whereby each microservice can implement a single functionality and can be independently managed and deployed. However, an undesirable side-effect of this modular design is the large state space of possibly inter-dependent configuration parameters (of the constituent microservices) which have to be tuned to improve application performance. This workshop paper investigates optimization techniques and dimensionality reduction strategies for tuning microservices applications, empirically demonstrating the significant tail latency improvements (as much as 23%) that can be achieved with configuration tuning.\",\"PeriodicalId\":269840,\"journal\":{\"name\":\"Proceedings of the 1st Workshop on Machine Learning and Systems\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1st Workshop on Machine Learning and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3437984.3458828\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1st Workshop on Machine Learning and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3437984.3458828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The microservice architecture allows applications to be designed in a modular format, whereby each microservice can implement a single functionality and can be independently managed and deployed. However, an undesirable side-effect of this modular design is the large state space of possibly inter-dependent configuration parameters (of the constituent microservices) which have to be tuned to improve application performance. This workshop paper investigates optimization techniques and dimensionality reduction strategies for tuning microservices applications, empirically demonstrating the significant tail latency improvements (as much as 23%) that can be achieved with configuration tuning.
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