A Robust Optimization Approach for Resource Allocation in Edge Computing-enabled Networks

The uncertain factors such as network status, measurement errors and quality of service (QoS) requirements of applications make it challenging to guarantee the performance of edge computing-enabled networks through resource allocation schemes modeled on accurate information. This paper investigates the impact of information uncertainty on resource allocation in edge computing-enabled networks. We model the resource constraints as chance constraints and jointly optimize wireless access point (AP) selection, computing node association, and traffic engineering to maximize the network utility. Since the problem contains uncertainty parameters and binary variables, it is intractable to solve. Therefore, we utilize the Bernstein approximation to derive convex conservative approximations for chance constraints. To address the unrealistic nature of the problem due to its large size and computing complexity, we employ the alternating direction method of multiplier to iterate wireless AP selection, computing node association, and bandwidth allocation in a distributed manner. Additionally, we use the convex optimization method to solve the corresponding sub-problems. Simulations are conducted to demonstrate that our proposed resource allocation scheme can satisfy more requirements and save more resources than other schemes.