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This paper studies the design and performance of the Prob- abilistic Priority (PP) packet scheduling algorithm to schedule packets. Unlike an earlier design that uses fractional arithmetic and prohibits large number of classes, we present an integer PP algorithm and show that PP is a special scheme of applying lottery scheduling to bandwidth allocation in a strict priority sense. We then propose a Multi-winner PP (MPP) scheduler using multi-winner lottery scheduling to improve the throughput and response time accuracy and a ∞exible ticket transfer algorithm to improve the deadline violation probability in probabilistic scheduling. Finally, we investigate the issue of parameter assignment for an MPP scheduler and use our techniques to implement a prototype Assured Forwarding (AF) mechanism in a network processor. In the Difierentiated Service (DifiServ) architecture, individual ∞ows with sim- ilar Quality-of-Service (QoS) requirements are aggregated, and given the same treatment as described by a Per-Hop-Behavior (PHB) in terms of QoS metrics such as average packet delay, packet loss and jitter. The routers do not keep per-∞ow states and there are no complex resource signaling mechanism involved (1). The Assured Forwarding (AF) PHB guarantees only that the assured tra-c is delivered with a higher probability than the best-efiort tra-c; in the case of severe network congestion, the assured tra-c can still experience severe losses and high delay. This paper analyzes the Probabilistic Priority (PP) scheduling discipline within the framework of relative service difierentiation. PP adopts a probabilistic relative service model. At every service round, each class takes a bid. Since higher priority classes have higher probabilities associated with them, in the long run, they will be served more often than lower priority classes. As compared to Strict Priority (SP), this increases fairness among classes and pre- vent the starvation of lower priority classes. We flrst show that PP is a cross application of lottery scheduling in a strict priority sense to provide propor- tional bandwidth sharing among classes. This in turn allows us to beneflt from numerous techniques presented in (7,8) to control PP. The lottery and stride

Achieving Relative Differentiated Services Using Proportional Probabilistic Priority Scheduling on Network Processor