REPA: Resilient and Proactive Resource Allocation for Last-Mile Heterogeneous Edge-IoT Systems

The emerging heterogeneous Edge-IoT systems are supporting various essential and critical applications in public safety, transportation, education, and health. The reliability and resiliency of such systems are crucial given the varied demands of IoT applications and the potential for edge failure. This paper tackles two key Edge-IoT resiliency challenges: i) how to cushion the impact of sudden workload increase and allow graceful performance degradation for heterogeneous applications; ii) how to better prepare for and handle severe disruptions to avoid significant application performance loss. These are complicated challenges due to limited edge resources, heterogeneity of IoT applications, and the dynamicity of the Edge-IoT environment. Therefore, we develop a novel intelligent resource allocation framework named REPA that is application-centric to enable proactive resiliency and offer graceful degradation of IoT applications. The new framework: 1) proactively cushions and absorbs the disruptive impacts of edge workload increases, and provides graceful application degradation in moderate to severe edge system loads, across multiple Edge-IoT systems; 2) features a two-level optimization model that jointly optimizes the intra-zone and inter-zone edge resource allocation such that the application’s performance degradation and service disruption are minimized; 3) includes novel deep reinforcement learning (DRL) designs for resource allocation that are specialized for the complex Edge-IoT environment and provide faster and stable decision-making capabilities than the existing methods. Performance evaluation demonstrates the ability of REPA to maintain the best performance of the IoT applications regardless of disruptive situations.