A Novel Cooperative Micro-Caching Algorithm Based on Fuzzy Inference Through NFV in Ultra-Dense IoT Networks

Minimizing transaction latency and network traffic is pivotal in large-scale Internet of Things (IoT) applications. This paper investigates the fundamentals of distributed caching, cache coordination, network function virtualization, fog computing, and software-defined networking to avoid service loss and enhance quality of experience (QoE) in IoT applications. We visualize caching as a virtual network function (VNF) and use fog nodes to persistently host a large number of micro-caches as VNFs in the vicinity of their interest locations. We formulate the cache placement and migration process as a multi integer linear programming (MILP) problem. Firstly, we propose a cache consensus function to decide whether a content needs caching or not. Secondly, we propose a fuzzy inference based algorithm to solve the MILP problem for dynamic placement and migration of micro-caches at appropriate locations in geographically co-located 5G radio access networks. Another significant contribution of the proposed scheme is the inter-RAN cooperation among micro-caches to augment service quality by mitigating network traffic. Simulation results show the superiority of the proposed scheme over existing approaches.