A multi-agent system based architecture for enabling Edge autonomous management.

Edge computing extends the Cloud computing paradigm by providing computation resources at the edge of the network where data is being generated to effectively meet the demands of real-time or latency-sensitive applications. Given Edge's particular characteristics, centralized controlling and decision making in the Cloud are no longer the only option. In contrast, it should be enabled throughout the Edge deployment's hierarchy from the top level of the hierarchy to near the IOT devices (at the edge of the network). This means enabling and enhancing intelligence and autonomy at the edge. This paper suggests an autonomous vision for Edge management. We propose a multi-agent system architecture, enabling autonomous decision making at Edge environments. A case study, using learning agents, is presented to illustrate the way the proposed solution enables sound management decisions. The case study focuses on smart offloading and autonomous power management. The profitability of enhancing intelligence and autonomy is proved in both cases. • Given Edge's particular characteristics, its control should be enabled throughout the Edge deployment's hierarchy from the top level of the hierarchy to near the IoT devices. • In Edge environments, the self-resource management is highly required since human intervention is impractical for such large Edge networks. • We argue not only for bringing computing resources closer to user devices and sensors as it was advocated by Edge computing, but also their management should be brought closer. • By calling up agent paradigm, we propose a multi-agent solution in order to deal with autonomous management at the Edge. • The profitability of enhancing intelligence and autonomy, by using learning agents, is proved in the case of smart offloading and autonomous power management. [ABSTRACT FROM AUTHOR]