This paper studies a Deadline-constrained task Offloading and resource Allocation Problem (DOAP) in a backhaul-network-enabled Mobile Edge Computing (MEC) system with both bandwidth and computational resource constraints. The goal is to maximize the total inference accuracy improvement across all tasks. We formulate this problem, denoted as $\mathbf{P}$, as an Integer Linear Programming (ILP) problem and show it is NP-hard via a reduction from the maximum-weight three-dimensional matching problem. To address $\mathbf{P}$, we develop $\mathtt{FastSA}$, a linear-time $\frac{1}{10}$-approximation algorithm based on the local-ratio technique. To the best of our knowledge, $\mathtt{FastSA}$ provides the first constant-factor performance guarantee for backhaul-network-enabled DOAPs. In addition, we introduce a fault detection and recovery framework that enables robust MEC operation under backhaul link and edge server failures. Experiments using profiled image classification workloads and real-world taxi traces show that $\mathtt{FastSA}$ achieves an average accuracy improvement of $25.6\%$ over benchmark algorithms under fault-free conditions while maintaining the lowest runtime. Under fault-injected scenarios, our framework reduces performance degradation by an average of $12.8\%$, demonstrating its effectiveness in fault handling.

翻译：暂无翻译