Data-Driven Online Resource Allocation for User Experience Improvement in Mobile Edge Clouds

As the cloud is pushed to the edge of the network, resource allocation for user experience improvement in mobile edge clouds (MEC) is increasingly important and faces multiple challenges. This paper studies quality of experience (QoE)-oriented resource allocation in MEC while considering user diversity, limited resources, and the complex relationship between allocated resources and user experience. We introduce a closed-loop online resource allocation (CORA) framework to tackle this problem. It learns the objective function of resource allocation from the historical dataset and updates the learned model using the online testing results. Due to the learned objective model is typically non-convex and challenging to solve in real-time, we leverage the Lyapunov optimization to decouple the long-term average constraint and apply the prime-dual method to solve this decoupled resource allocation problem. Thereafter, we put forth a data-driven optimal online queue resource allocation (OOQRA) algorithm and a data-driven robust OQRA (ROQRA) algorithm for homogenous and heterogeneous user cases, respectively. Moreover, we provide a rigorous convergence analysis for the OOQRA algorithm. We conduct extensive experiments to evaluate the proposed algorithms using the synthesis and YouTube datasets. Numerical results validate the theoretical analysis and demonstrate that the user complaint rate is reduced by up to 100% and 18% in the synthesis and YouTube datasets, respectively.

I. Introduction

With the rapid development of wireless communications, there is a huge increase in the data generated in future networks. Simultaneously, the advanced storage and computing technologies strongly demand real-time data processing [2], [3]. Motivated by this trend, mobile edge cloud (MEC), which is also known as mobile edge computing, has emerged as an effective technology to alleviate these issues by placing multiple edge servers proximate to the base station (BS) [4]. The benefits of the MEC system are twofold: i) improve bandwidth utilization and energy efficiency for communications; ii) reduce transmission delay and the demand for backhaul links. In recent years, MEC has attracted increasing attention in wireless communications, predicted to increase by 26.4% annually from 2022 to 2026 [5].