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.

TrapFetch is trained by monitoring the read requests issued by an application. It detects bursts of disk reads, determines the appropriate addresses at which breakpoints should be inserted in the application and library codes prior to the bursts of reads, and then logs this information with the data requested during the interval between each consecutive pair of breakpoints. When the application and library codes are loaded from the disk into memory, TrapFetch inserts breakpoints at the designated addresses based on the logs. During subsequent runs, TrapFetch is invoked at each breakpoint when it prefetches the corresponding data into the page cache. This approach is effective during both launch and run-time. TrapFetch operates at the user level, thus avoiding interference with other applications. In experiments on five popular applications (FlightGear, SpeedDreams 2, Pillars of Eternity, Eclipse, and VegaStrike), TrapFetch reduced the time for launch by up to 39.7% and time for run-time data-loading by up to 63.7%.