{"title":"走向分布式边缘系统","authors":"S. Dustdar, Ilir Murturi","doi":"10.1109/CogMI50398.2020.00021","DOIUrl":null,"url":null,"abstract":"In the past few years, researchers from academia and industry stakeholders suggest adding more computational resources (i.e., storage, networking, and processing) closer to the end-users and IoT domain, respectively, at the edge of the network. Such computation entities perceived as edge devices aim to overcome high-latency issues between the cloud and the IoT domain. Thus, processing IoT data streams closer to the end-users and IoT domain can solve several operational challenges. Since then, a plethora of application-specific IoT systems are introduced, mainly hard-coded, inflexible, and limited extensibility for future changes. Additionally, most IoT systems maintain a centralized design to operate without considering the dynamic nature of edge networks. In this paper, we discuss some of the research issues, challenges, and potential solutions to enable: i) deploying edge functions on edge resources in a distributed manner and ii) deploying and scaling edge applications on-premises of Edge-Cloud infrastructure. Additionally, we discuss in detail the three-tier Edge-Cloud architecture. Finally, we introduce a conceptual framework that aims to enable easy configuration and deployment of edge-based systems on top of heterogeneous edge infrastructure and present our vision within a smart city scenario.","PeriodicalId":360326,"journal":{"name":"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Towards Distributed Edge-based Systems\",\"authors\":\"S. Dustdar, Ilir Murturi\",\"doi\":\"10.1109/CogMI50398.2020.00021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the past few years, researchers from academia and industry stakeholders suggest adding more computational resources (i.e., storage, networking, and processing) closer to the end-users and IoT domain, respectively, at the edge of the network. Such computation entities perceived as edge devices aim to overcome high-latency issues between the cloud and the IoT domain. Thus, processing IoT data streams closer to the end-users and IoT domain can solve several operational challenges. Since then, a plethora of application-specific IoT systems are introduced, mainly hard-coded, inflexible, and limited extensibility for future changes. Additionally, most IoT systems maintain a centralized design to operate without considering the dynamic nature of edge networks. In this paper, we discuss some of the research issues, challenges, and potential solutions to enable: i) deploying edge functions on edge resources in a distributed manner and ii) deploying and scaling edge applications on-premises of Edge-Cloud infrastructure. Additionally, we discuss in detail the three-tier Edge-Cloud architecture. Finally, we introduce a conceptual framework that aims to enable easy configuration and deployment of edge-based systems on top of heterogeneous edge infrastructure and present our vision within a smart city scenario.\",\"PeriodicalId\":360326,\"journal\":{\"name\":\"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CogMI50398.2020.00021\",\"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 Second International Conference on Cognitive Machine Intelligence (CogMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CogMI50398.2020.00021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In the past few years, researchers from academia and industry stakeholders suggest adding more computational resources (i.e., storage, networking, and processing) closer to the end-users and IoT domain, respectively, at the edge of the network. Such computation entities perceived as edge devices aim to overcome high-latency issues between the cloud and the IoT domain. Thus, processing IoT data streams closer to the end-users and IoT domain can solve several operational challenges. Since then, a plethora of application-specific IoT systems are introduced, mainly hard-coded, inflexible, and limited extensibility for future changes. Additionally, most IoT systems maintain a centralized design to operate without considering the dynamic nature of edge networks. In this paper, we discuss some of the research issues, challenges, and potential solutions to enable: i) deploying edge functions on edge resources in a distributed manner and ii) deploying and scaling edge applications on-premises of Edge-Cloud infrastructure. Additionally, we discuss in detail the three-tier Edge-Cloud architecture. Finally, we introduce a conceptual framework that aims to enable easy configuration and deployment of edge-based systems on top of heterogeneous edge infrastructure and present our vision within a smart city scenario.