An Efficient Resource Allocation Scheme With Uncertain Network Status in Edge Computing-Enabled Networks

Collaborative resource allocation is crucial for reducing overhead and enhancing resource utilization in edge computing-enabled networks. To ensure a satisfactory user experience, we recognize the importance of considering information uncertainty in resource allocation. Therefore, we explore information uncertainty in edge computing-enabled networks, especially within the complex environment of resource coupling. However, existing methods lack a comprehensive and robust solution for coordinating wireless, transport, and computing resource under this information uncertainty. This paper addresses this gap by proposing a joint optimization of access point (AP) selection, computing node association, and traffic engineering, aiming to maximize network utility under the uncertain conditions of wireless status and application QoS requirements. The constraints under these uncertainties are modeled as chance constraints, complicating the problem's solvability. We adopt the Bernstein approximation to establish convex conservative approximations of the chance constraints. Given the problem's substantial size and computational complexity, the alternating direction method of multipliers is employed to solve the approximated problem in a distributed manner. We further derive the closed solutions of the corresponding sub-problems. Extensive simulations validate the superiority of our proposed scheme, demonstrating its ability to achieve a good trade-off between meeting user requirements and optimizing resource utilization.