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

TL;DR

This paper proposes a joint optimization framework for computing offloading and resource allocation in MEC systems to enhance classification task performance, balancing accuracy and delay.

Contribution

It introduces a novel joint optimization approach for offloading and resource allocation tailored to intelligent classification tasks in MEC systems, with a convex optimization-based solution.

Findings

Significant performance improvements over benchmarks.

Effective trade-off between system revenue and cost.

Closed-form solutions for resource allocation problems.

Abstract

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.