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Akats: A System for Resilient Deployments on Edge Computing Environments Using Federated Machine Learning Techniques

Authors
  1. Josu Díaz-de-Arcaya
  2. Ana I. Torre-Bastida
  3. Lander Bonilla
  4. Juan López de Armentia
  5. Raul Miñón
  6. Gorka Zarate
  7. Aitor Almeida

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Abstract

Edge computing is a game changer for IoT, as it allows IoT devices to independently process and analyze data instead of just sending it to the cloud. But managing this considerable number of devices and deploying workloads on them in a coordinated and intelligent manner remains a challenge nowadays. In this paper, we focus on introducing the resilience dimension into these deployments, and we provide two main contributions: the use of federated machine learning techniques to develop a collaborative tool between the different devices aimed at detecting the possibility of a device failure, and subsequently, the utilization of the inferred information to optimize deployment plans ensuring the resilience in the devices. These two advances are implemented in an intelligent system, Akats, whose architecture is described in detail in this article. Finally, an application scenario is presented, based on Industry 4.0 - Machine predictive maintenance, to exemplify the benefits of the proposed intelligent system.