A Feasibility Study of Bandwidth Smoothing on the World-Wide Web Using Machine Learning

Abstract

The bandwidth usage due to HTTP traffic often varies considerably over the course of a day, requiring high network performance during peak periods while leaving network resources unused during off-peak periods. We show that using these extra network resources to prefetch web content during off-peak periods can significantly reduce peak bandwidth usage without compromising cache consistency. With large HTTP traffic variations it is therefore feasible to apply ``bandwidth smoothing’’ to reduce the cost and the required capacity of a network infrastructure. In addition to reducing the peak network demand, bandwidth smoothing improves cache hit rates. We calculate the potential reduction in bandwidth for a given bandwidth usage profile, and show that a simple hueristic has poor prefetch accuracy. We then apply machine learning techniques to automatically develop prefetch strategies that have high accuracy. Our results are based on web proxy traces generated at a large corporate Internet exchange point and data collected from recent scans of popular web sites.

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