Scalable URL Matching with Small Memory Footprint

URL matching lies at the core of many networking applications and Information Centric Networking architectures. For example, URL matching is extensively used by Layer 7 switches, ICN/NDN routers, load balancers, and security devices. Modern URL matching is done by maintaining a rich database that consists of tens of millions of URL which are classified to dozens of categories (or egress ports). In real-time, any input URL has to be searched in this database to find the corresponding category.
In this paper, we introduce a generic framework for accurate URL matching (namely, no false positives or miscategorization) that aims to reduce the overall memory footprint, while still having low matching latency. We introduce a dictionary-based compression method that compresses the database by 60%, while having only a slight overhead in time. Our framework is very flexible and it allows hot-updates,
cloud-based deployments, and can deal with strings that are not URLs.