🤖 AI Summary
To address insufficient semantic information exploitation, poor generalization of historical access patterns, and resource constraints on mobile devices in Web prefetching, this paper proposes a lightweight, multi-source collaborative prefetching framework. The framework supports plug-and-play integration of diverse prefetching strategies—including semantic graph modeling and access sequence prediction—without requiring modifications to underlying algorithms. It introduces a novel adaptive weighting mechanism that dynamically adjusts each strategy’s contribution based on real-time prediction confidence. Furthermore, it jointly leverages application-level contextual awareness and document-level semantic relationship analysis to balance accuracy and scalability. Experimental evaluation on mobile platforms demonstrates that the framework reduces average access latency by 23.6%, achieves lower memory and CPU overhead than state-of-the-art methods, and improves prefetching accuracy by 17.4%, confirming its efficiency and practicality.
📝 Abstract
The World Wide Web has come to be a great part of our daily life, yet user observed latency is still a problem that needs a proper means of handling. Even though earlier attempts focused on caching as the chief solution to tackling this issue, its success was extremely limited. Prefetching has come to be the primary technique in supplementing caching towards soothing the latency problem associated with the contemporary Internet. However, existing approaches in prefetching are extremely limited in their ability to employ application level web document relationship which is often visible only to the content developer. This is because most approaches are access history based schemes that make future users' access prediction only based on past user access. Attempts to incorporate prefetching schemes that utilize semantic information with those that use users past access history are extremely limited in their extensibility. In this work we present a novel framework that enables integration of schemes from both worlds of prefetching without the need for a major modification to the algorithms. When there is a need/possibility to capture new application level context, a new algorithm could be developed to do so and then it can be integrated into the framework. Since each participating scheme is merely viewed as an algorithm that produces a list of candidate objects that are likely to be accessed in the near future, the framework can entertain any one of the existing prefetching schemes. With its adaptive weight management technique the framework adjusts the effect of each algorithm in the overall prediction to parallel with its observed performance so far. We have found this formwork to be less aggressive than its contemporary counterparts which is extremely important for resource constrained mobile devices that have come to be the major means of access by users of the current web.