Abstract
Previously, a class of solutions including core-stateless fair queueing (CSFQ), rainbow fair queueing, and Diffserv have been proposed to address the scalability concerns that have plagued stateful architectures such as Intserv and fair queueing. However, despite some desirable properties, these solutions still have serious scalability, robustness, and deployment problems. Their scalability, suffers from the fact that the core cannot transcend trust boundaries (such as at ISP-ISP interconnects), and so the high-speed routers on these boundaries must maintain per flow or per aggregate state. The lack of robustness is because a single malfunctioning edge or core router could severely impact the performance of the entire network. The deployability is hampered because the set of routers must be carefully configured with a well-defined set of edge routers surrounding the core. In this paper, we propose an approach to address these limitations. The main idea is to use statistical verification to identify and contain the flows whose packets carry incorrect information. To demonstrate the applicability of this approach we develop an extension of CSFQ, called self-verifying CSFQ (SV-CSFQ). With SV-CSFQ, rate estimation is performed by sending hosts, and all routers statistically verify these rate estimates. Statistical verification allows routers to identify misbehaving flows and routers, and thereby protect other flows. This makes our approach robust and highly scalable as it eliminates the need for stateful routers at trust boundaries, and for the core-edge distinction. We present simulations and analysis of the performance of this approach, and discuss its general applicability to provide other scalable and robust network services.