🤖 AI Summary
This study addresses the lack of theoretical understanding regarding the interaction between congestion control algorithms (CCAs) and traffic policers in modern networks, which hinders the rational configuration of policing parameters. It is the first to systematically demonstrate that the interaction dynamics between CCAs and policers—whether based on virtual queues or token buckets—fundamentally differ from those with traffic shapers. The authors develop a formal analytical framework that integrates congestion control theory, virtual queue mechanisms, and token bucket models to derive precise configuration guidelines for key policing parameters, such as virtual queue capacity and assured rate thresholds. This work provides network operators with verifiable and tunable policing strategies, substantially enhancing the efficiency of network resource management.
📝 Abstract
We describe details of a formal framework to study the interaction between traffic policers, implemented using phantom queues or token buckets, and any arbitrary congestion control algorithm (CCA). This framework allows network providers to figure out configurations for their traffic policers (phantom queue size, safe rate thresholds, etc.). We also use this framework to describe why CCAs interact differently with a traffic policer compared to traffic shapers.