In the edge computing paradigm, mobile devices offload the computational tasks to an edge server by routing the required data over the wireless network. The full potential of edge computing becomes realized only if a smart device selects the most appropriate server in terms of the latency and energy consumption, among many available ones. The server selection problem is challenging due to the randomness of the environment and lack of prior information about the environment. Therefore, a smart device, which sequentially chooses a server under uncertainty, aims to improve its decision based on the historical time and energy consumption. The problem becomes more complicated in a dynamic environment, where key variables might undergo abrupt changes. To deal with the aforementioned problem, we first analyze the required time and energy to data transmission and processing. We then use the analysis to cast the problem as a budget-limited multi-armed bandit problem, where each arm is associated with a reward and cost, with time-variant statistical characteristics. We propose a policy to solve the formulated problem and prove a regret bound. The numerical results demonstrate the superiority of the proposed method compared to a number of existing solutions.

中文翻译：

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用于不确定性动态网络中的节能和延迟敏感边缘计算的多臂老虎机

在边缘计算范式中，移动设备通过在无线网络上路由所需的数据，将计算任务卸载到边缘服务器。只有当智能设备在许多可用服务器中选择最合适的服务器时，边缘计算的全部潜力才能实现。由于环境的随机性和缺乏有关环境的先验信息，服务器选择问题具有挑战性。因此，智能设备在不确定的情况下顺序选择服务器，旨在根据历史时间和能源消耗改进其决策。在动态环境中，问题变得更加复杂，其中关键变量可能会发生突然变化。针对上述问题，我们首先分析数据传输和处理所需的时间和精力。然后，我们使用分析将问题转化为预算受限的多臂老虎机问题，其中每个臂都与奖励和成本相关联，具有时变统计特征。我们提出了一个策略来解决公式化的问题并证明一个遗憾界限。数值结果证明了所提出的方法与许多现有解决方案相比的优越性。