A Lyapunov-Based Framework for Smart Accuracy Latency Tradeoff in Three-Layer Edge Computing Systems

Smart cities and their associated applications integrate various cutting-edge technologies, such as machine learning (ML) and edge computing (EC). ML-driven services within smart cities are often computationally intensive and time-sensitive. While EC serves as a complementary infrastructure to traditional cloud-end users architecture, enhancing the quality-of-service (QoS) for end users, the advent of ML-dependent services necessitates a redefinition of QoS to incorporate relevant ML performance metrics. On the other side, the limited computational capabilities of EC servers make such QoS metrics conflicting. Therefore, there is a tradeoff between system cost for running ML models and ML model performance. In this article, we present an effective offloading scheme designed for a three-layer cloud–edge–users system, aiming to optimize the performance-QoS tradeoff in the long run, especially for ML-dependent services, considering the dynamic of the system. Leveraging Lyapunov optimization techniques, we transform the offloading problem from a long-term perspective into a frame-based problem. Recognizing that the new per-frame problem is NP-hard, we introduce a tabu search-based algorithm, termed TSMALE, to address this challenge. Analytically, we demonstrate that our proposed TSMALE algorithm achieves an approximate solution of roughly 0.923 compared to the optimal solution.