Computing Offloading and Semantic Compression for Intelligent Computing Tasks in MEC Systems

TL;DR

This paper proposes an optimization framework for intelligent computing offloading and semantic compression in MEC systems, improving resource utilization and task accuracy through convex optimization techniques.

Contribution

It introduces a joint optimization model for offloading and semantic compression, with solutions derived via convex optimization and successive convex approximation.

Findings

Algorithm converges quickly in simulations.

Achieves better resource utilization than benchmarks.

Improves system utility in terms of accuracy and delay.

Abstract

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.