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

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.