QoS-aware 5G component selection for content delivery in multi-access edge computing

Abstract

The demand for content such as multimedia services with stringent latency requirements has proliferated significantly, posing heavy backhaul congestion in mobile networks. The integration of Multi-access Edge Computing (MEC) and 5G network is an emerging solution that alleviates the backhaul congestion to meet QoS requirements such as ultra-low latency, ultra-high reliability, and continuous connectivity to support various latency-critical applications for user equipment (UE). Content caching can markedly augment QoS for UEs by increasing the availability of popular content. However, uncertainties originating from user mobility cause the most challenging barrier in deciding content routes for UEs that lead to minimum latency. Considering the 5G-enabled MEC components, it is critical to select the optimal 5G components, representing content routes from Edge Application Servers (EASs) to UEs, that enhances QoS for the UEs with uncertain mobility patterns by reducing frequent handover (path reallocation). To this aim, we study the component selection for QoS-aware content delivery in 5G-enabled MEC. We first formulate an integer programming (IP) optimization model to obtain the optimal content routing decisions. As this problem is NP-hard, we tackle its intractability by designing an efficient online learning approach, called Q-CSCD, to achieve a bounded performance. Q-CSCD learns the optimal component selection for UEs and autonomously makes decisions to minimize latency for content delivery. We conduct extensive experiments based on a real-world dataset to validate the effectiveness of our proposed algorithm. The results reveal that Q-CSCD leads to low latency and handover ratio in a reasonable time with a reduced regret over time.