A Novel Predictive-Collaborative-Replacement (PCR) Intelligent Caching Scheme for Multi-Access Edge Computing

Abstract

Multi-Access Mobile Edge Computing (MEC) is proclaimed as a key technology for reducing service processing delays in 5G networks. One of the use cases in MEC is content caching as a way of bringing resources closer to the end-users. Consequently, both latency and QoE are reduced. However, MEC has a limited storage space compared to the cloud. Therefore, there is a need to effectively manage the cache storage. This article proposes and evaluates a novel scheme (PCR) that combines proactive prediction, collaboration among MECs and replacement algorithm to manage content caching in MEC. Results show that the proposed replacement scheme outperforms conventional baseline content caching algorithms LFU, LRU, MQ, FBR, LFRU. This has been validated with experimental results using a real dataset (MovieLens20M dataset) and comparison with contemporary Long Short Term Memory (LSTM) based caching algorithm.

Keywords

• Edge intelligence
• intelligent caching
• MEC
• predictive caching
• LSTM