AI

End-to-end Verification of QoS Policies

Abstract

Configuring a large number of routers and network devices to achieve quality of service (QoS) goals is a challenging task. In a differentiated services (DiffServ) environment, traffic flows are assigned specific classes of service, and service level agreements (SLA) are enforced at routers within each domain. We present a model for QoS configurations that facilitates efficient property-based verification. Network configuration is given as a set of policies governing each device. The model efficiently checks the required properties against the current configuration using computation tree logic (CTL) model checking. By symbolically modeling possible decision paths for different flows from source to destination, properties can be checked at each hop, and assessments can be made on how closely configurations adhere to the specified agreement. The model also covers configuration debugging given a specific QoS violation. Efficiency and scalability of the model are analyzed for policy per-hop behavior (PHB) parameters over large network configurations.