3.8 Proceedings Paper

Management, orchestration and workflow automation of Edge Computing services: The TANDEM approach

Publisher

IEEE
DOI: 10.1109/EuCNC/6GSummit54941.2022.9815637

Keywords

Edge Computing; Function as a Service (Function-as-a-Service (FaaS)); Service Chain Orchestrator; Artificial Intelligence (AI); Internet of Things (IoT)

Funding

  1. TANDEM (Function and Device as a Service Support for Edge Computing) project
  2. European Union
  3. Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH CREATE -INNOVATE [T2E.K-02825]

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Edge computing, a growing distributed computing paradigm, is widely adopted in various domains such as cloud computing, telecommunications, IoT, and AI. However, automation in deployment and operation of edge computing services is still immature, making practical scenarios complex. This paper presents an integrated platform approach to simplify the establishment and management of edge computing services, with a focus on IoT.
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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