Decentralized Edge Workload Forecasting With Gossip Learning

Abstract

Edge computing has emerged as a crucial paradigm for addressing the growing demands of interconnected devices and large-scale mobile applications by relocating computation and storage services closer to end-users. Edge workloads are inherently volatile and challenging to forecast due to their dependence on factors such as human mobility patterns and geographically-distributed infrastructure, combined with the dynamic nature of edge nodes. Traditional centralized approaches to workload forecasting are inadequate in the context of decentralized and failure-prone edge environments. To address this challenge, this paper investigates workload forecasting using Gossip Learning (GL), an asynchronous peer-to-peer learning protocol. GL allows for the training of forecasting models in a fully-decentralized manner, thereby mitigating single point of failure risks and enhancing overall system robustness. We extended the original protocol across multiple dimensions to improve convergence, reduce communication overhead, and enhance resilience to failures. We evaluated the proposed approach through extensive simulations; the obtained results demonstrate its effectiveness with respect to classical methods, rendering it a promising solution to enhance load balancing and task offloading strategies at the edge, thereby ensuring Quality-of-Service (QoS) and reducing Service Level Agreement (SLA) violations.