Wenzhao Zhang, Yuxuan Zhang, Hongchang Fan, Yi Gao, Wei Dong, Jinfeng Wang
{"title":"TinyEdge:为物联网应用提供快速边缘系统定制","authors":"Wenzhao Zhang, Yuxuan Zhang, Hongchang Fan, Yi Gao, Wei Dong, Jinfeng Wang","doi":"10.1109/SEC50012.2020.00008","DOIUrl":null,"url":null,"abstract":"Customizing and deploying an edge system is a time-consuming and complex task, considering the hardware heterogeneity, third-party software compatibility, diverse performance requirements, etc. In this paper, we present TinyEdge, a holistic system for the rapid customization of edge systems. The key idea of TinyEdge is to use a top-down approach for designing the software and estimating the performance of the customized edge systems under different hardware specifications. Developers select and conFigure modules to specify the critical logic of their interactions, without dealing with the specific hardware or software. Taking the configuration as input, TinyEdge automatically generates the deployment package and estimate the performance after sufficient profiling. TinyEdge provides a unified customization framework for modules to specify their dependencies, functionalities, interactions, and configurations. We implement TinyEdge and evaluate its performance using real-world edge systems. Results show that: 1) TinyEdge achieves rapid customization of edge systems, reducing 44.15% of customization time and 67.79% lines of code on average compared with the state-of-the-art edge platforms; 2) TinyEdge builds compact modules and optimizes the latent circular dependency detection and message queuing efficiency; 3) TinyEdge performance estimation has low average absolute error in various settings.","PeriodicalId":375577,"journal":{"name":"2020 IEEE/ACM Symposium on Edge Computing (SEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"TinyEdge: Enabling Rapid Edge System Customization for IoT Applications\",\"authors\":\"Wenzhao Zhang, Yuxuan Zhang, Hongchang Fan, Yi Gao, Wei Dong, Jinfeng Wang\",\"doi\":\"10.1109/SEC50012.2020.00008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Customizing and deploying an edge system is a time-consuming and complex task, considering the hardware heterogeneity, third-party software compatibility, diverse performance requirements, etc. In this paper, we present TinyEdge, a holistic system for the rapid customization of edge systems. The key idea of TinyEdge is to use a top-down approach for designing the software and estimating the performance of the customized edge systems under different hardware specifications. Developers select and conFigure modules to specify the critical logic of their interactions, without dealing with the specific hardware or software. Taking the configuration as input, TinyEdge automatically generates the deployment package and estimate the performance after sufficient profiling. TinyEdge provides a unified customization framework for modules to specify their dependencies, functionalities, interactions, and configurations. We implement TinyEdge and evaluate its performance using real-world edge systems. Results show that: 1) TinyEdge achieves rapid customization of edge systems, reducing 44.15% of customization time and 67.79% lines of code on average compared with the state-of-the-art edge platforms; 2) TinyEdge builds compact modules and optimizes the latent circular dependency detection and message queuing efficiency; 3) TinyEdge performance estimation has low average absolute error in various settings.\",\"PeriodicalId\":375577,\"journal\":{\"name\":\"2020 IEEE/ACM Symposium on Edge Computing (SEC)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE/ACM Symposium on Edge Computing (SEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SEC50012.2020.00008\",\"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/ACM Symposium on Edge Computing (SEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SEC50012.2020.00008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
TinyEdge: Enabling Rapid Edge System Customization for IoT Applications
Customizing and deploying an edge system is a time-consuming and complex task, considering the hardware heterogeneity, third-party software compatibility, diverse performance requirements, etc. In this paper, we present TinyEdge, a holistic system for the rapid customization of edge systems. The key idea of TinyEdge is to use a top-down approach for designing the software and estimating the performance of the customized edge systems under different hardware specifications. Developers select and conFigure modules to specify the critical logic of their interactions, without dealing with the specific hardware or software. Taking the configuration as input, TinyEdge automatically generates the deployment package and estimate the performance after sufficient profiling. TinyEdge provides a unified customization framework for modules to specify their dependencies, functionalities, interactions, and configurations. We implement TinyEdge and evaluate its performance using real-world edge systems. Results show that: 1) TinyEdge achieves rapid customization of edge systems, reducing 44.15% of customization time and 67.79% lines of code on average compared with the state-of-the-art edge platforms; 2) TinyEdge builds compact modules and optimizes the latent circular dependency detection and message queuing efficiency; 3) TinyEdge performance estimation has low average absolute error in various settings.