SGRM: Stackelberg Game-Based Resource Management for Edge Computing Systems

Abstract

The incessant technological advancements of recent Internet of Things (IoT) networks have led to a rapidly increasing number of connected devices and workloads. Resource management is a key technique for such systems to operate efficiently. In this paper, we present SGRM, a game theory-based framework for dynamic resource management of IoT networks under CPU, memory, bandwidth and latency constraints. SGRM combines a novel execution time prediction mechanism along with Stackelberg games and Vickrey auctions in order to tackle the multi-objective problem of task offloading in a competitive Edge Computing system. We design, implement and evaluate our novel game theory-based framework over a real IoT system for a diverse set of interference scenarios and varying devices, showing that i) the proposed prediction mechanism can provide accurate predictions, achieving 2.3% absolute percentage error on average, ii) SGRM achieves near-optimal results and outperforms alternative solutions by up to 66.6% and iii) SGRM provides scalable, real-time and lightweight performance characteristics.