With recent advancements in edge computing capabilities, there has been a significant increase in utilizing the edge cloud for event-driven and time-sensitive computations. However, large-scale edge computing networks can suffer substantially from unpredictable and unreliable computing resources which can result in high variability of service quality. We consider the problem of computation offloading over unknown edge cloud networks with a sequence of timely computation jobs. Motivated by the MapReduce computation paradigm, we assume that each computation job can be partitioned to smaller Map functions which are processed at the edge, and the Reduce function is computed at the user after the Map results are collected from the edge nodes. We model the service quality of each edge device as function of context. The user decides the computations to offload to each device with the goal of receiving a recoverable set of computation results in the given deadline. By leveraging the coded computing framework in order to tackle failures or stragglers in computation, we formulate this problem using contextual-combinatorial multi-armed bandits (CC-MAB), and aim to maximize the cumulative expected reward. We propose an online learning policy called online coded edge computing policy, which provably achieves asymptotically-optimal performance in terms of regret loss compared with the optimal offline policy for the proposed CC-MAB problem. In terms of the cumulative reward, it is shown that the online coded edge computing policy significantly outperforms other benchmarks via numerical studies.

中文翻译：

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黑暗中的边缘计算：利用上下文组合强盗和编码计算


随着边缘计算能力的最新进步，利用边缘云进行事件驱动和时间敏感计算的情况显着增加。然而，大规模边缘计算网络可能会受到不可预测和不可靠的计算资源的严重影响，这可能导致服务质量的高度可变性。我们考虑通过一系列及时计算作业在未知边缘云网络上进行计算卸载的问题。受 MapReduce 计算范式的启发，我们假设每个计算作业可以划分为在边缘处理的更小的 Map 函数，并且在从边缘节点收集 Map 结果后在用户处计算 Reduce 函数。我们将每个边缘设备的服务质量建模为上下文的函数。用户决定将计算卸载到每个设备，目标是在给定期限内接收一组可恢复的计算结果。通过利用编码计算框架来解决计算中的失败或落后者，我们使用上下文组合多臂老虎机（CC-MAB）来制定这个问题，并旨在最大化累积预期奖励。我们提出了一种称为在线编码边缘计算策略的在线学习策略，与所提出的 CC-MAB 问题的最优离线策略相比，该策略在遗憾损失方面可证明实现了渐近最优性能。在累积奖励方面，通过数值研究表明，在线编码边缘计算策略显着优于其他基准。