Predictive Caching in Non-Stationary Environments: A Time Series Prediction and Survival Analysis Approach

Abstract

This paper introduces an innovative predictive caching strategy tailored to a real-world dataset, specifically the Facebook video dataset. Making caching decisions for the dataset is challenging due to its dynamic nature, where users’ content requests vary over time without fitting into any known models. Traditional caching strategies, which often rely on a constant pool of files, do not suit this dataset as content is requested by users, and then its popularity fades over time; furthermore, the list of available content changes. We propose a two-stage predictive caching strategy. Initially, it forecasts the number of user requests using content features and historical request data, achieved through training a long short-term memory (LSTM) network. Then, we employ our proposed extended Cox proportional hazard (E-CPH) model to predict the survival probability of content. This facilitates proactive content caching. Caching new content is made possible by the timely eviction of content unlikely to be requested again. To incorporate the predicted content popularity and its life cycle into the caching decision, we introduce a partially observable Markov decision process (POMDP)-based caching strategy. Here, the survival probability of content contributes to the belief state of the associated content which leads to our believed predicted reward - a cache hit. The caching algorithm then stores the files based on their predicted believed reward taking into account both the popularity and survival probability predictions. Simulation results validate the efficacy of our proposed predictive caching method in enhancing the cache hit rate compared to conventional recurrent neural network (RNN)-based caching and policy-based caching approaches, such as least frequently used caching and its variants.