High Performance Fair Bandwidth Allocation for Resilient Packet Rings

The Resilient Packet Ring (RPR) IEEE 802.17 standard is under development as a new high-speed backbone technology for metropolitan area networks. A key performance objective of RPR is to simultaneously achieve high utilization, spatial reuse, and fairness, an objective not achieved by current technologies such as SONET and Gigabit Ethernet nor by legacy ring technologies such as FDDI. The core technical challenge for RPR is the design of a bandwidth allocation algorithm that dynamically achieves these properties. The difficulty is in the distributed nature of the problem, that upstream ring nodes must inject traffic at a rate according to congestion and fairness criteria downstream. Unfortunately, the proposed algorithms in the current draft standards have a number of critical limitations. For example, we show that in a two-flow two-link scenario with unbalanced and constant-rate traffic demand, a draft RPR algorithm will suffer from dramatic bandwidth oscillations within nearly the entire range of the link capacity. Moreover, such oscillations hinder spatial reuse and decrease throughput significantly. In this paper, we introduce a new dynamic bandwidth allocation algorithm called Distributed Virtual-time Scheduling in Rings (DVSR). The key idea is for nodes to compute a simple lower bound of temporally and spatially aggregated virtual time using per-ingress counters of packet (byte) arrivals. We show that with this information propagated along the ring, each node can remotely approximate the ideal fair rate for its own traffic at each downstream link. Hence, DVSR flows rapidly converge to their ring-wide fair rates while maximizing spatial reuse. To evaluate DVSR, we bound the deviation in service between DVSR and an idealized reference model, thereby bounding the unfairness. With simulations, we find that compared to current techniques, DVSR’s convergence times are an order of magnitude faster (e.g., 2 vs. 50 msec), oscillations are mitigated (e.g., ranges of 1% vs. up to 100%), and nearly complete spatial reuse is achieved (e.g., 0.1% throughput loss vs. 14%).