I-HARF: Intelligent and Hierarchical Framework for Adaptive Resource Facilitation in Edge-IoT Systems

Abstract

Edge computing is being used to facilitate closer computing, storage, and networking resources to support various IoT applications including delay-sensitive ones. It is envisioned that the future Edge-IoT systems will incorporate heterogeneous IoT devices distributed over multiple geographical zones of certain institutions with edge resource demands that vary according to time and location. Edge servers (resource facilitators) are with limited resources and are susceptible to “outlandish” situations, such as service overloading, outage, and external attacks; they may also have to handle the roaming of IoT devices among different zones. These situations induce the need for alternative edge servers using an adaptive resource facilitation scheme to fulfill the demands of the IoT applications. In this article, we develop a novel intelligent and hierarchical resource facilitation framework named I-HARF that adapts to dynamic Edge-IoT situations, including outlandish situations, mobility, application’s sensitivity, and varying resource demand of IoT applications based on time and location. I-HARF achieves an adaptive facilitation and holistically addresses the facilitation technical barriers by: 1) adopting the hierarchical structure which efficiently migrates the resource facilitation from intrazone to interzone levels; 2) extending novel intrazone and interzone optimization models to boost the utilities of the edge servers and the IoT applications; and 3) developing a novel and unique actor dual-critic and collective actor–critic deep reinforcement learning (DRL) designs that intelligently facilitate the edge resources in both intrazone and interzone, respectively. The evaluation results demonstrate I-HARF’s capability enabling adaptive resource facilitation that adjusts according to the dynamic Edge-IoT situations.