Real-time content analysis is typically a bottleneck in Web filtering. To accelerate the filtering process, this work presents a simple, but effective early decision algorithm that analyzes only part of the Web content. This algorithm can make the filtering decision, either to block or to pass the Web content, as soon as it is confident with a high probability that the content really belongs to a banned or an allowed category. Experiments show the algorithm needs to examine only around one-fourth of the Web content on average, while the accuracy remains fairly good: 89% for the banned content and 93% for the allowed content. This algorithm can complement other Web filtering approaches, such as URL blocking, to filter the Web content with high accuracy and efficiency. Text classification algorithms in other applications can also follow the principle of early decision to accelerate their applications.

Managing increasing traffic from Instant Messengers and P2P applications is becoming more important nowadays. We present an in-kernel architecture of management gateway, namely kP2PADM, built upon open-source packages with several modifications and design techniques. First, the in-kernel design streamlines the data path through the gateway. Second, the dual-queue buffer eliminates head-of-line blocking for multiple connections. Third, a connection cache reduces useless reconnection attempts from the peers. Fourth, a fast-pass mechanism avoids slowing down the TCP transmission. The in-kernel design approximately doubles the throughput of the design in the user space. The internal benchmarks also analyze the impact of each function on performance.