Design, Analysis and Implementation of a Novel Multiple Resource Scheduler

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Over the past decade, the problem of achieving fair bandwidth allocation on a link shared by multiple traffic flows has been extensively researched. However, as these flows traverse a computer network, they share many different kinds of resources, such as links, buffers, and router CPU. The ultimate goal should hence be overall fairness in the allocation of multiple resources rather than a single specific resource such as link bandwidth. In this paper, we present a novel scheduler, called prediction-based composite fair queuing (PCFQ), which jointly allocates the fair share of the link bandwidth and processing resources to all competing flows. We derive the worst-case delay bound, the work complexity, and the relative fairness bound for the PCFQ scheduler and show that it outperforms a system consisting of separate bandwidth and CPU schedulers. We further present simulation results which illustrate the improved performance characteristics achieved by PCFQ. We also demonstrate that our composite scheduler can be easily implemented on an off-the-shelf network processor such as the Intel IXP 2400. Experimental results from the IXP 2400 implementation highlight the effectiveness and high performance of this algorithm in a real-world system.