Opportunistic Prefetching of Cellular Internet of Things (cIoT) Device Contexts

The number of IoT devices is expected to be between 32-99 Billion by 2025, many of which will use the cellular wireless data network for communications. This presents a unique challenge to the operator while allocating resources, namely how to optimally balance CPU and memory usage in virtualized and physical hosts while simultaneously handling millions of IoT devices without affecting the quality of experience of normal mobile users. Due to the sheer number of the IoT devices, it is not feasible to store their session context in memory. In this work, we present a machine learning model that predicts the network usage pattern of five broad classes of cIoT devices. The prediction model trained on a Multilayer Perceptron allows the network operator to opportunistically prefetch cIoT context from secondary storage before it is required. Further, we propose a new metric -- Value of Perfect Information -- to assess our approach. We evaluate our approach across two fronts: First, we study the efficacy of replacement algorithms such as LRU, MRU, FIFO and random replacement; we also assess the impact of varying memory slots. Finally, we evaluate our models against the default (no prefetching) model and an on-time prefetching model to demonstrate the value of our pre-fetching approach.