This paper investigates the intelligent computing task-oriented computing offloading and semantic compression in mobile edge computing (MEC) systems. With the popularity of intelligent applications in various industries, terminals increasingly need to offload intelligent computing tasks with complex demands to MEC servers for computing, which is a great challenge for bandwidth and computing capacity allocation in MEC systems. Considering the accuracy requirement of intelligent computing tasks, we formulate an optimization problem of computing offloading and semantic compression. We jointly optimize the system utility which are represented as computing accuracy and task delay respectively to acquire the optimized system utility. To solve the proposed optimization problem, we decompose it into computing capacity allocation subproblem and compression offloading subproblem and obtain solutions through convex optimization and successive convex approximation. After that, the offloading decisions, computing capacity and compressed ratio are obtained in closed forms. We design the computing offloading and semantic compression algorithm for intelligent computing tasks in MEC systems then. Simulation results represent that our algorithm converges quickly and acquires better performance and resource utilization efficiency through the trend with total number of users and computing capacity compared with benchmarks.

翻译：本文研究了移动边缘计算（MEC）系统中面向智能计算任务的计算卸载与语义压缩问题。随着智能应用在各行业的普及，终端设备愈发需要将具有复杂需求的智能计算任务卸载至MEC服务器进行处理，这对MEC系统中的带宽与计算能力分配构成了巨大挑战。考虑智能计算任务的精度要求，我们提出了计算卸载与语义压缩的联合优化问题。通过分别优化表示为计算精度与任务延迟的系统效用，我们获取了最优系统效用。为求解该优化问题，将其分解为计算容量分配子问题和压缩卸载子问题，并利用凸优化与逐次凸逼近方法获得解。进而，得到了卸载决策、计算容量与压缩比的闭合表达式。随后，我们设计了面向MEC系统中智能计算任务的计算卸载与语义压缩算法。仿真结果表明，与基准方案相比，所提算法能快速收敛，并在用户总数与计算容量变化趋势下获得更优性能与资源利用效率。