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.

翻译：移动边缘计算（MEC）通过将计算与数据存储能力下沉至用户近端，实现了低时延高带宽应用。智能计算是MEC的重要应用场景，其利用计算资源根据任务需求解决智能任务相关问题。然而，由于任务需求与MEC资源之间存在复杂的交互关系，如何在MEC系统中高效地实现智能任务的计算卸载与资源分配仍是一个具有挑战性的问题。针对该挑战，我们研究了MEC系统中面向智能任务的联合计算卸载与资源分配问题。目标是通过联合优化计算精度与任务时延以最大化系统效用，从而实现系统性能最优。我们聚焦分类智能任务，同时考虑任务的精度需求与MEC系统的并行计算能力，构建了一个优化问题。为求解该优化问题，将其分解为三个子问题：子载波分配、计算能力分配与压缩卸载。我们利用凸优化与逐次凸近似方法，推导出子载波分配、卸载决策、计算能力与压缩比的闭式表达式。基于所提求解方案，设计了一种面向MEC系统智能任务的高效计算卸载与资源分配算法。仿真结果表明，与基准方案相比，所提算法显著提升了MEC系统中智能任务的性能，并在考虑智能任务的情况下实现了系统收益与成本之间的灵活权衡。