{"title":"摘要:通过超参数优化自动部署正确的大小","authors":"Aniruddha Rakshit, Jayson G. Boubin","doi":"10.1145/3576842.3589157","DOIUrl":null,"url":null,"abstract":"Internet of Things (IoT) and Edge deployments are diverse, complex, and highly constrained. These properties make correctness difficult or impossible to verify a priori. We present early work on an automatic deployment right-sizing tool for edge and IoT deployments. Our tool uses the PROWESS testbed to accurately emulate candidate deployment form-factors, and optimizes deployment parameters to minimize costs. We show that our early work finds optimal deployment configurations 6.3X faster than Bayesian optimization, a state of the art hyperparameter optimization technique.","PeriodicalId":266438,"journal":{"name":"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Poster Abstract: Automatic Deployment Right-Sizing Through Hyperparameter Optimization\",\"authors\":\"Aniruddha Rakshit, Jayson G. Boubin\",\"doi\":\"10.1145/3576842.3589157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Internet of Things (IoT) and Edge deployments are diverse, complex, and highly constrained. These properties make correctness difficult or impossible to verify a priori. We present early work on an automatic deployment right-sizing tool for edge and IoT deployments. Our tool uses the PROWESS testbed to accurately emulate candidate deployment form-factors, and optimizes deployment parameters to minimize costs. We show that our early work finds optimal deployment configurations 6.3X faster than Bayesian optimization, a state of the art hyperparameter optimization technique.\",\"PeriodicalId\":266438,\"journal\":{\"name\":\"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3576842.3589157\",\"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 8th ACM/IEEE Conference on Internet of Things Design and Implementation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3576842.3589157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Poster Abstract: Automatic Deployment Right-Sizing Through Hyperparameter Optimization
Internet of Things (IoT) and Edge deployments are diverse, complex, and highly constrained. These properties make correctness difficult or impossible to verify a priori. We present early work on an automatic deployment right-sizing tool for edge and IoT deployments. Our tool uses the PROWESS testbed to accurately emulate candidate deployment form-factors, and optimizes deployment parameters to minimize costs. We show that our early work finds optimal deployment configurations 6.3X faster than Bayesian optimization, a state of the art hyperparameter optimization technique.