We consider the latency minimization problem in a task-offloading scenario, where multiple servers are available to the user equipment for outsourcing computational tasks. To account for the temporally dynamic nature of the wireless links and the availability of the computing resources, we model the server selection as a multi-armed bandit (MAB) problem. In the considered MAB framework, rewards are characterized in terms of the end-to-end latency. We propose a novel online learning algorithm based on the principle of optimism in the face of uncertainty, which outperforms the state-of-the-art algorithms by up to ~1s. Our results highlight the significance of heavily discounting the past rewards in dynamic environments.

中文翻译：

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非平稳环境中计算卸载的在线算法

我们考虑任务卸载场景中的延迟最小化问题，其中多个服务器可供用户设备用于外包计算任务。为了说明无线链路的时间动态特性和计算资源的可用性，我们将服务器选择建模为多臂老虎机 (MAB) 问题。在所考虑的 MAB 框架中，奖励的特征在于端到端延迟。我们基于面对不确定性的乐观原则提出了一种新颖的在线学习算法，该算法的性能优于最先进的算法高达约 1 秒。我们的结果强调了在动态环境中大幅降低过去奖励的重要性。