Cloud-Assisted Dynamic and Cooperative Content Caching in Mobile Edge Computing

We are heading toward a data-centric world and a huge volume of data is getting generated due to the growing number of mobile smart devices. As a result, it is affecting the network latency by putting pressure on the back-haul network. A number of new latency sensitive applications are also emerging that can not tolerate significant network latency added by the remote cloud. Therefore, a new network paradigm MEC (Mobile edge computing, also termed as Multi-access edge computing) is getting popular where computing and storage resources can be placed at the edge of the network to solve the problem of the latency sensitive applications. The MEC can also be used to filter the data to reduce the pressure on the backhaul network. While content caching can provide improvement in terms of latency, frequent updates to the nodes’ storage can lead to significant overhead in terms of pricing, performance and even energy consumption as has been investigated in earlier studies. Such overheads become more significant due to the fact that unlike cloud computing, edge computing nodes are constrained by limited resources. Thus, there is a need for caching approaches that can provide maximum hit percent with minimum overhead. In this article, we propose an algorithm in an attempt to find such a balance between the hit percent and cost. Then we evaluate our algorithm on a real-world request trace of a music streaming service. Experimental results show that our algorithm gives better cache hit percentage and significantly lowers cost as compared to baseline algorithms.