论文标题
用于计算在非平稳环境中卸载的在线算法
An Online Algorithm for Computation Offloading in Non-Stationary Environments
论文作者
论文摘要
我们考虑在任务缩减方案中的延迟最小化问题，其中用户设备可用于外包计算任务的多个服务器。为了说明无线链接的时间动态性质和计算资源的可用性，我们将服务器选择建模为多臂强盗（MAB）问题。在经过考虑的MAB框架中，奖励的特征是端到端的延迟。我们根据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，该算法的表现胜过最新的算法。我们的结果突出了在动态环境中重大打折过去奖励的重要性。
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.
