This paper addresses the problem of distributed task offloading centred at individual user terminals in a cellular multi-access edge computing (MEC) system. We introduce an online learning-assisted algorithm based on distributed bandit optimization (DBO) to cope with time-varying cost and time-varying constraint functions with unknown statistics on-the-go. The proposed algorithm jointly exploits the projected dual gradient iterations and a greedy method as well as a single broadcast communicating the MEC states to the users at the end of each decision cycle to minimize task computing-communication delay in the long run at user terminals. To track the performance of the proposed online learning algorithm over time, we define a dynamic regret to assess the closeness of the underlying delay cost of the DBO to a clairvoyant dynamic optimum, and an aggregate violation metric to evaluate the asymptotic satisfaction of the constraints. We derive lower and upper bounds for dynamic regret as well as an upper-bound for the aggregate violation and show that the upper-bounds are sub-linear under sub-linear accumulated hindsight variations. The simulation results and comparisons confirm the effectiveness of the proposed algorithm in the long run.

中文翻译：

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多接入边缘计算中分布式任务卸载的在线学习算法


本文解决了蜂窝多址边缘计算（MEC）系统中以单个用户终端为中心的分布式任务卸载问题。我们引入了一种基于分布式强盗优化（DBO）的在线学习辅助算法，以应对时变成本和时变约束函数以及未知统计数据。所提出的算法联合利用了投影双梯度迭代和贪婪方法以及在每个决策周期结束时将 MEC 状态传达给用户的单个广播，以最大限度地减少用户终端长期运行的任务计算通信延迟。为了跟踪所提出的在线学习算法随着时间的推移的性能，我们定义了一个动态后悔来评估 DBO 的潜在延迟成本与透视动态最优的接近程度，并定义了一个聚合违规度量来评估约束的渐近满足程度。我们推导出动态遗憾的下限和上限以及总体违规的上限，并表明上限在亚线性累积后见变化下是亚线性的。仿真结果和比较证实了所提出算法的长期有效性。