Freshness-aware computation offloading has garnered great attention recently in the edge computing arena, with the aim of promptly obtaining up-to-date information and minimizing the transmission of outdated data. However, most of the existing work assumes that wireless channels are reliable and neglect the dynamics and stochasticity thereof. In addition, varying priorities of offloading tasks along with heterogeneous computing units also pose significant challenges in effective task scheduling and resource allocation. To address these challenges, we cast the freshness-aware task offloading problem as a multi-priority optimization problem, considering the unreliability of wireless channels, the heterogeneity of edge servers, and prioritized users. Based on the nonlinear fractional programming and ADMM-Consensus method, we propose a joint resource allocation and task offloading algorithm to solve the original problem iteratively. To improve communication efficiency, we further devise a distributed asynchronous variant for the proposed algorithm. We rigorously analyze the performance and convergence of the proposed algorithms and conduct extensive simulations to corroborate their efficacy and superiority over the existing baselines.

翻译：面向新鲜度的计算卸载近年来在边缘计算领域受到广泛关注，其目标是及时获取最新信息并减少陈旧数据的传输。然而，现有大部分工作假设无线信道可靠，忽略了其动态性和随机性。此外，卸载任务的不同优先级以及异构计算单元的存在，也为有效的任务调度和资源分配带来了重大挑战。为应对这些挑战，本文将面向新鲜度的任务卸载问题建模为一个多优先级优化问题，同时考虑无线信道不可靠性、边缘服务器异构性以及用户优先级。基于非线性分数规划与ADMM-Consensus方法，我们提出了一种联合资源分配与任务卸载算法，以迭代方式求解原问题。为提升通信效率，我们进一步为该算法设计了一种分布式异步变体。我们对所提算法的性能与收敛性进行了严格分析，并通过大量仿真验证了其相对于现有基线方法的有效性与优越性。