Abstract
Recent years have seen the rapid adoption of Internet of Things (IoT) technologies, where billions of physical devices are interconnected to provide data sensing, computing and actuating capabilities. IoT-based systems have been extensively deployed across various sectors, such as smart homes, smart cities, smart transport, smart logistics and so forth. Newer paradigms such as edge computing are developed to facilitate computation and data intelligence to be performed closer to IoT devices, hence reducing latency for time-sensitive tasks. However, IoT applications are increasingly being deployed in remote and difficult to reach areas for edge computing scenarios. These deployment locations make upgrading application and dealing with software failures difficult. IoT applications are also increasingly being deployed as containers which offer increased remote management ability but are more complex to configure. This paper proposes an approach for effectively managing, updating and re-configuring container-based IoT software as efficiently, scalably and reliably as possible with minimal downtime upon the detection of software failures. The approach is evaluated using docker container-based IoT application deployments in an edge computing scenario.
Highlights
The past decade has seen the rapid development and adoption of the Internet of Things (IoT)
The redeployment processes/steps across multiple IoT devices are described as follows: 1. The deployment controller sends a redeployment request for an IoT device to the Cloud server
The Cloud server forwards the request to the IoT gateways
Summary
The past decade has seen the rapid development and adoption of the Internet of Things (IoT). Service requirements for IoT systems have motivated the development of new computational paradigms such as edge and fog computing to facilitate IoT data computation and analysis, bringing data intelligence and decision-making processes closer to IoT sensors and actuators, improving their performance by reducing service latency [9,10,11,12]. This is important for time-sensitive tasks, such as those in autonomous vehicles, manufacturing and transport industries, where minute delays in services can have serious safety consequences [13]. The data from the sensors would be passed onto a edge device that forwards the data to the cloud or minor data processing occurs at the edge which is forwarded to the cloud [24]
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