World Wide Web (WWW) traffic will dominate network traffic for the foreseeable future. Accurate predictions of network performance can only be achieved if network models reflect WWW traffic statistics. Through analysis of usage logs at a range of caches it is shown that WWW traffic is not a Poisson arrival process, and that it displays significant levels of self-similarity. It is also shown for the first time that the self-similar variability extends to demand for individual pages, and is far more pervasive than previously thought. These measurements are used as the basis for a cache-modelling tool-kit. Using this software the impact of the variability on predictive planning is illustrated. The model predicts that optimisations based on predictive algorithms (such as least recently used discard) are likely to reduce performance very quickly. This means that, far from improving the efficiency of the network, conventional approaches to network planning and engineering will tend to reduce efficiency and increase costs.