Analyzing Web Robots and Their Impact on Caching

Abstract

Understanding the nature and the characteristics of Web robots is an essential step to analyze their impact on caching. Using a multi-layer hierarchical workload model, this paper presents a characterization of the workload generated by autonomous agents and robots. This characterization focuses on the statistical properties of the arrival process and on the robot behavior graph model. A set of criteria is proposed for identifying robots in real logs. We then identify and characterize robots from real logs applying a multi-layered approach. Using a stack distance based analytical model for the interaction between robots and Web site caching, we assess the impact of robots' requests on Web caches. Our analyses point out that robots cause a significant increase in the miss ratio of a server-side cache. Robots have a referencing pattern that completely disrupts locality assumptions. These results indicate not only the need for a better understanding of the behavior of robots, but also the need of Web caching policies that treat robots' requests differently than human generated requests.