{"title":"Quality of Service Assurance for Internet of Things Time-Critical Cloud Applications: Experience with the Switch and Entice Projects","authors":"S. Taherizadeh, V. Stankovski","doi":"10.1109/IIAI-AAI.2017.209","DOIUrl":null,"url":null,"abstract":"Various Internet of Things (IoT) applications, such as home automation and disaster early warning systems, are being introduced in various areas of human life and business. Today, a common method for delivery of such applications is via component-based software engineering disciplines based on cloud computing technologies such as containers. However, there are still numerous technological challenges to be solved particularly related to the time-critical Quality of Service (QoS) aspects of such applications. Runtime variations in the workload intensity as the amount of service tasks to be processed may radically affect the application performance perceived by the end-users or lead to the underutilization of resources. In order to assure the QoS of these containerized applications, monitoring is required at both container and application levels. Currently, there is a great lack of such multi-level monitoring systems. In this study, we present an architecture and implementation of a multi-level monitoring framework to ensure system health and adapt an IoT application in response to varying quantity, size and computational requirements of arrival requests. In this work, cloud application adaptation possibility includes horizontal scaling of container-based application instances.","PeriodicalId":281712,"journal":{"name":"2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIAI-AAI.2017.209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Various Internet of Things (IoT) applications, such as home automation and disaster early warning systems, are being introduced in various areas of human life and business. Today, a common method for delivery of such applications is via component-based software engineering disciplines based on cloud computing technologies such as containers. However, there are still numerous technological challenges to be solved particularly related to the time-critical Quality of Service (QoS) aspects of such applications. Runtime variations in the workload intensity as the amount of service tasks to be processed may radically affect the application performance perceived by the end-users or lead to the underutilization of resources. In order to assure the QoS of these containerized applications, monitoring is required at both container and application levels. Currently, there is a great lack of such multi-level monitoring systems. In this study, we present an architecture and implementation of a multi-level monitoring framework to ensure system health and adapt an IoT application in response to varying quantity, size and computational requirements of arrival requests. In this work, cloud application adaptation possibility includes horizontal scaling of container-based application instances.