Joint Computing Offloading and Resource Allocation for Classification Intelligence Tasks in MEC Systems

Mobile edge computing (MEC) facilitates high reliability and low-latency applications by bringing computation and data storage closer to end-users. Intelligent computing is an important application of MEC, where computing resources are used to solve intelligent task-related problems based on task requirements. However, efficiently offloading computing and allocating resources for intelligent tasks in MEC systems is a challenging problem due to complex interactions between task requirements and MEC resources. To address this challenge, we investigate joint computing offloading and resource allocation for classification intelligence tasks (CITs) in MEC systems. Our goal is to optimize system utility by jointly considering computing accuracy and task delay to achieve maximum utility of our system. We focus on CITs and formulate an optimization problem that considers task characteristics including the accuracy requirements and the parallel computing capabilities in MEC systems. To solve the proposed problem, we decompose it into three subproblems: subcarrier allocation, computing capacity allocation and compression offloading. We use successive convex approximation and convex optimization method to derive optimized feasible solutions for the subcarrier allocation, offloading variable, computing capacity allocation, and compression ratio. Based on our solutions, we design an efficient joint computing offloading and resource allocation algorithm for CITs in MEC systems. Our simulation demonstrates that the proposed algorithm significantly improves the performance by 16.4% on average and achieves a flexible trade-off between system revenue and cost considering CITs compared with benchmarks.