On the Fairness-Efficiency Tradeoff for Packet Processing with Multiple Resources

Middleboxes are widely deployed in today’s networks. They ap-ply a variety of complex network functions to transform, filter, and optimize incoming traffic based on the payload of packets. These functions require the support of multiple types of resources, such as CPU and link bandwidth, for processing incoming packets. Hence, a multi-resource packet scheduling algorithm is needed to allow flows to share these resources fairly and efficiently. However, un-like traditional fair queueing where bandwidth is the only concern, we show in this paper that fairness and efficiency are conflicting objectives that cannot be achieved simultaneously in the presence of multiple resources. Ideally, a scheduling algorithm should al-low network operators to flexibly specify their fairness and effi-ciency requirements, so as to meet the Quality of Service demands while keeping the system at a high utilization level. Yet, existing multi-resource scheduling algorithms focus on fairness only, and may lead to poor resource utilization. In this paper, we propose a new scheduling algorithm to achieve a flexible tradeoff between fairness and efficiency for packet processing, consuming both CPU and link bandwidth. Experimental results based on both real-world implementation and trace-driven simulation suggest that trading off a modest level of fairness can potentially improve the efficiency to the point where the system capacity is almost saturated