  Mobile edge computing (MEC) enables low-latency and high-bandwidth
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 intelligent tasks in MEC systems. Our goal is to optimize system
utility by jointly considering computing accuracy and task delay to achieve
maximum system performance. We focus on classification intelligence tasks and
formulate an optimization problem that considers both the accuracy requirements
of tasks and the parallel computing capabilities of MEC systems. To solve the
optimization problem, we decompose it into three subproblems: subcarrier
allocation, computing capacity allocation, and compression offloading. We use
convex optimization and successive convex approximation to derive closed-form
expressions for the subcarrier allocation, offloading decisions, computing
capacity, and compressed ratio. Based on our solutions, we design an efficient
computing offloading and resource allocation algorithm for intelligent tasks in
MEC systems. Our simulation results demonstrate that our proposed algorithm
significantly improves the performance of intelligent tasks in MEC systems and
achieves a flexible trade-off between system revenue and cost considering
intelligent tasks compared with the benchmarks.
