Robust Task Offloading and Resource Allocation Under Imperfect Computing Capacity Information in Edge Intelligence Systems

Abstract

In edge intelligence systems, task offloading and resource allocation policies critically depend on the required computing capacity of the task, which can only be accurately measured after execution, presenting significant design challenges. In this paper, we address the problem of robust task offloading and resource allocation under imperfect computing capacity information, where the exact value as well as distribution knowledge of the required computing capacity cannot be obtained in advance. Specifically, we formulate the energy-time cost (ETC) minimization problem using min-max robust optimization. To tackle this challenging issue, we propose a decoupling method. This method first assumes the offloading policy is predetermined and derives two independent subproblems: local ETC and edge ETC. Then, we provide a closed-form optimal solution for the local ETC problem. The edge ETC problem is equivalently transformed into a geometric programming (GP) problem, and we introduce an effective iterative algorithm to obtain a stationary point, utilizing successive convex approximation (SCA). Finally, we design a coordinate descent (CD)-based algorithm to optimize the offloading policy effectively. Extensive simulations demonstrate that the proposed policy significantly outperforms other benchmark methods, achieving near-optimal performance even in the presence of high estimation errors in computing capacity.