Intentionality-related Deep Learning Method in Web Prefetching

Many prediction models have been proposed to improve the effectiveness of web prefetching for reducing the response time perceived by users when browsing the web. Most of these models are based on structure learning and are applied at the client side. Currently, considerable attention is being paid to proxy-based prefetching because it is more effective and accurate in predicting the correlated pages of many websites of similar interest for more homogeneous users. Compared with client-based prefetching, more complex prediction tasks must run in the proxy, which implies that a more powerful prediction model is required. Thus, based on the time-series characteristics of browsing records, we proposed the intentionality-related long short-term memory (Ir-LSTM) model, which combines both the Skip-Gram embedding method and the LSTM model while expanding the input features with user information. We also propose a novel dynamic allocation module for detecting real-time traffic bursts and correspondingly adjusting the correlation coefficient of the model's output to achieve higher server-side resource utilization while fully maximizing hit ratio