Adaptive Hierarchical Cache Management for Cloud RAN and Multi-access Edge Computing in 5G Networks

Abstract

Cloud Radio Access Networks (CRAN) and Multi-Access Edge Computing (MEC) are two vital technologies proposed for 5G mobile networks. CRAN utilizes the cloud model on top of the traditional RAN, thus provides scalability, flexibility, and improved resource utilization. MEC brings cloud computing services closer to the users to provide high bandwidth, low latency, real-time access and to improve the overall user experience. A cache may be included in both CRAN and MEC architectures to speed up time-critical communications as well as to provide preferential services. This work focuses on adaptive cache management of CRAN and MEC. First, a new hierarchical cache management algorithm, H-EXD-AHP (Hierarchical Exponential Decay and Analytical Hierarchy Process), is proposed to improve the existing EXD-AHP. Next, two adaptive versions of hierarchy-based cache management algorithms are described, with the goal to provide guaranteed-QoS. Finally, a third enhanced adaptive version is described to greatly speed up the run time. Performance evaluation of the new adaptive algorithms based on system parameters provided by Nokia Research are carried out with comparison with existing ones. Experimental results show that the new hierarchical H-EXD-AHP improves the performance in terms of increased cache hit rate as well as reduced network overhead and access delay. Furthermore, the three adaptive algorithms are effective in guaranteeing QoS and in reducing algorithm execution time. This work contributes significantly in realizing the support of 5G for IoT by enhancing CRAN and MEC performance and would have wide applications in other real-time systems that require efficient adaptive cache management with guaranteed QoS.