Data-Driven Analytics Task Management at the Edge: A Fuzzy Reasoning Approach

Abstract

Dynamic data-driven applications such as tracking and surveillance have emerged in the Internet of Things (IoT) environments. Such applications rely heavily on data generated by connected devices (e.g., sensors). Consequently, leveraging these data in building data-driven predictive analytics tasks improves the Quality of Service (QoS) and, as a result, Quality of Experience (QoE). Such data support various data-driven tasks such as regression and classification. Analytics tasks require data and resources to be executed at the edge since transferring them to the cloud negatively affects response times and QoS. However, the network edge is characterized by limited resources compared to the cloud, being the subject of constraints that are violated upon offloading data-driven tasks to improper edge nodes. We contribute with an analytics task management mechanism based on the context of the requested data, the task delay sensitivity, and the VM utilization. We introduce a novel Fuzzy inference mechanism for determining whether data-driven tasks should be executed locally, offloaded to peer edge servers, or sent to the cloud. We showcase how our fuzzy reasoning mechanism efficiently derives such decisions by calculating the offloading probability per task. The derived optimal actions are compared against benchmark models in Edge Computing (EC).