Abstract
The distributed computing paradigm that brings information processing closer to end users and data sources, i.e., Edge Computing, is growing in popularity and adoption in many different domains, such as Cloud Computing, Telecommunications, Internet of Things (IoT) and (distributed) Artificial Intelligence (AI). Despite several efforts towards standardizing Edge Computing services and their ecosystem-driven interactions, especially from the telecom world and open-source communities, automation in deployment, operation and interoperability of Edge Computing services is still in an immature state, making practical Edge/IoT scenarios that involve multiple application services and technologies, complex and cumbersome. This paper presents our approach of an integrated platform aimed to simplify the establishment, management, control and monitoring of edge computing services with a particular focus on the IoT domain. Central in this effort are the ability to (I) make use of existing functions and modules in new edge computing services and (II) to seamlessly integrate service function chain components with remote backend services and with locally available Edge/IoT devices for on-device processing. Characteristic examples of desired functions and local processing tasks, include data stream analytics, event-driven workflows and Machine Learning tasks with an emphasis on video stream analysis for Object Detection and/or tracking, for which we provide a deployment architecture of our approach.
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