Adaptive Workload Orchestration in Pure Edge Computing: A Reinforcement-Learning Model

Abstract

Edge computing is a promising paradigm that can address the requirements of compute-intensive tasks generated by delay-sensitive applications, through bringing processing and storage to the edge of the network. Task offloading is challenging in open and dynamic environments where applications with various Service Level Agreement (SLA) and Quality of Service (QoS) requirements frequently produce a fluctuated workload at the edge of a network with heterogeneous, mobile, and geodistributed nodes.The current literature has addressed this challenge by offloading tasks to fog or Mobile Edge Computing (MEC) servers. However, in strictly delay-sensitive applications such as augmented reality, autonomous driving, or remote surgery, a Pure Edge Computing (PEC) paradigm that allows peer-to-peer communication and cooperation is more reasonable.This paper proposes a novel learning-based task offloading model that enables a pure edge-based system with mobile and resource-constrained nodes to accommodate fluctuating workload generated by applications with various SLAs and QoS. The results show a better utilization of resources and tasks success rate when compared to the state-of-the-art algorithms.