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Abstract-A good crossbar switch scheduler should be able to achieve 100% throughput and maintain fairness among competing flows. A pure input-queued (IQ) non-buffered switch requires an impractically complex scheduler to achieve this goal. Common solutions are to use crossbar speedup and/or buffered crossbar.In this paper, we explore this issue in a buffered crossbar without speedup. We first discuss the conflict between fairness and throughput and the fairness criteria in crossbar switch scheduling, and justify that a desirable scheduler should sustain full bandwidth for admissible traffic and ensure max-min fairness for non-admissible traffic. Then we describe an adaptive maxmin fair scheduling (AMFS) algorithm and show by analysis and simulation that it can provide both 100% throughput and max-min fairness. Finally we briefly discuss the hardware implementation of the AMFS algorithm.Index Terms-switch scheduling, buffered crossbar, combined input crosspoint queued (CICQ) switch, quality of service (QoS), max-min fairness.

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Provisioning Quality of Service (QoS) across an aggregate of transmission entities (e.g. link aggregation) or processing elements (e.g. network processors) is a challenging problem. The difficulty lies in simultaneously satisfying fairness to flows (with different bandwidth requirements) and ensuring minimized intra-flow reordering. This problem is crucial to many applications (that utilize parallel communication or processing paths) like multi-path load distribution, multi-path storage I/O, web service, data processing by network processors in the datapath of routers, trans-coding multimedia flow traffic content over the Internet to name a few. We present two algorithms for multi-link systems that aim at reducing undesired reordering, ensure fair sharing of flows and optimal utilization of the links. Our algorithms are based on a new approach of dynamically partitioning flows among links. We perform simulations on real Internet traces to validate our algorithmic approach.

We address the Quality of Service (QoS) provisioning problem in an aggregated multi-server environment. The input packet stream traffic to the system is categorized at a macro level into a few "flow-classes" that require service differentiation; each class is further subdivided into "flows" at the micro-level. Flows can be serviced by any server: however; the service rate is a function of the class and the particular server. Given such an environment, we have a multi-criteria optimization objective (i) provide differentiated service to flows (ii) achieve load balancing of the servers and (iii) maximize their throughput (amount of bytes serviced). We present an on-line fluid-based approximation scheme to schedule packets. Modeling the accumulated traffic in a class as fluid we use linear programming to first determine the optimal fractions that should be directed to different servers while ensuring fairness and high server throughput. We propose a packet scheduling strategy for the multi-server framework (by extending a well-known fair round-robin algorithm for single link systems) that effectively incorporates the optimal service fractions determined in the previous step. We validate the proposed algorithm with extensive simulations. The results show that the algorithm imparts high throughput with good service differentiation. We also evaluate reordering of packet requests within flow streams by presenting relevant metrics that quantify reordering.