Distributed Task Management in the Heterogeneous Fog: A Socially Concave Bandit Game

Xiao Cheng, S. Maghsudi
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引用次数: 1

Abstract

Fog computing has emerged as a potential solution to accommodate the explosive computational demand of mobile users. This potential mainly stems from the capacity of task offloading and allocation at the network edge, reducing the delay and improving the quality of service. However, optimizing the performance of a fog network is often challenging, when consider the distinct abilities and capacities of fog nodes. We study the distributed task allocation problem in such a heterogeneous fog computing network under noises. We formulate the problem as a social-concave game, where the players attempt to minimize their regret while converging to Nash equilibrium. We develop a no-regret strategy for task allocation. The strategy, namely bandit gradient ascent with momentum, is an online convex optimization algorithm with bandit feedback. Theoretical and numerical analysis show the superior performance of the proposed strategy for efficient task allocation compared to the state-of-the-art methods.
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异构雾中的分布式任务管理:一个社会凹凹的强盗博弈
雾计算已经成为一种潜在的解决方案,以适应移动用户的爆炸性计算需求。这种潜力主要源于网络边缘的任务卸载和分配能力,减少了延迟,提高了服务质量。然而,当考虑到雾节点的不同能力和容量时,优化雾网络的性能通常是具有挑战性的。研究了噪声条件下异构雾计算网络中的分布式任务分配问题。我们将这个问题描述为一个社会凹博弈,玩家试图在收敛于纳什均衡的同时最小化他们的后悔。我们制定了一个无悔的任务分配策略。该策略即强盗梯度动量上升策略是一种带强盗反馈的在线凸优化算法。理论和数值分析表明,与现有方法相比,所提出的策略在高效任务分配方面具有优越的性能。
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