The widespread adoption of edge computing has emerged as a prominent trend for alleviating task processing delays and reducing energy consumption. However, the dynamic nature of network conditions and the varying computation capacities of edge servers (ESs) can introduce disparities between computation loads and available computing resources in edge computing networks, potentially leading to inadequate service quality. To address this challenge, this paper investigates a practical scenario characterized by dynamic task offloading. Initially, we examine traditional Multi-armed Bandit (MAB) algorithms, namely the $\varepsilon$-greedy algorithm and the UCB1-based algorithm. However, both algorithms exhibit certain weaknesses in effectively addressing the tidal data traffic patterns. Consequently, based on MAB, we propose an adaptive task offloading algorithm (ATOA) that overcomes these limitations. By conducting extensive simulations, we demonstrate the superiority of our ATOA solution in reducing task processing latency compared to conventional MAB methods. This substantiates the effectiveness of our approach in enhancing the performance of edge computing networks and improving overall service quality.

翻译：边缘计算的广泛应用已成为缓解任务处理延迟和降低能耗的显著趋势。然而，网络条件的动态变化以及边缘服务器（ES）计算能力的差异可能导致边缘计算网络中计算负载与可用计算资源之间的失衡，进而引发服务质量不足的问题。为应对这一挑战，本文研究了一个以动态任务卸载为特征的现实场景。首先，我们考察了传统的多臂老虎机（MAB）算法，即$\varepsilon$-贪心算法和基于UCB1的算法。然而，这两种算法在处理潮汐式数据流量模式时均存在一定缺陷。因此，基于MAB，我们提出了一种自适应任务卸载算法（ATOA），该算法克服了上述局限性。通过开展大量仿真实验，我们证明了与传统的MAB方法相比，ATOA方案在降低任务处理延迟方面具有优越性。这证实了我们的方法在提升边缘计算网络性能及改善整体服务质量方面的有效性。