Intelligent Web Prefetching to Reduce Client Latencies

Rutgers University New Brunswick, New Brunswick NJ

Investigators

Abstract

Delays on the World-Wide Web are a well-known problem for users. Caching of web objects closer to clients is a technique shown to improve performance because requested objects that are in the cache can be presented to the client without traversing sometimes slow network connections to a possibly overloaded web server. Prefetching, in advance of their need, soon-to-be-requested objects that are not in the cache can improve performance even further. Such objects can often be predicted from a range of information sources, such as client and server histories as well as the contents of the objects currently and previously retrieved by the client. In this work, the researcher proposes to develop intelligent prefetching algorithms that use machine learning techniques to develop models that make predictions based on past experience, and to test their implementation in a proxy cache with the goal of reducing user-perceived latency. Moreover, adding prefetching to a cache raises some subtle problems for evaluation methods, and so the researcher proposes a new methodology for proxy cache evaluation that can also handle prefetching systems. The high-level goals of this research are to propose and implement both prefetching algorithms and a sufficient evaluation methodology for such algorithms. This evaluation methodology will then be used to measure the progress made toward the ultimate objective of reducing user latencies.