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Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks With Uncertainty

机译:具有不确定性的动态网络中节能和延迟敏感边缘计算的多武装匪

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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 same. 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 several online learning algorithms.
机译:在边缘计算范例中,移动设备通过通过无线网络路由所需数据来将计算任务卸载到边缘服务器。仅当智能设备在延迟和能量消耗方面选择最合适的服务器时,边缘计算的全部潜力才能实现。服务器选择问题由于环境的随机性以及缺乏关于相同的先前信息而挑战。因此,在不确定性下顺序选择服务器的智能设备旨在基于历史时间和能量消耗来改善其决定。在动态环境中,问题变得更加复杂,其中键变量可能会发生突然的变化。要处理上述问题,我们首先将所需的时间和精力分析到数据传输和处理。然后,我们使用分析将问题作为预算有限的多武装强盗问题,其中每个臂与奖励和成本相关联,具有时间变体统计特征。我们提出了一个解决方案问题的政策,并证明了遗憾。数值结果证明了与几种在线学习算法相比的提出方法的优越性。

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