acm sigcomm

This paper presented a router architecture design that consists of a complex software based controller and a simple fast packet forwarder. By leveraging the Zipf's property of the Internet traffic, authors proposed to improve router performance by passing a small number of heavy hitter traffic flows to the fast packet forwarder and hence offloading the software controller from most packets. The idea itself seems to be very simple. The authors showed that the top 1000 heavy hitter prefixes capture over 50% of traffic. The controller can be easily configured to offload these top heavy hitter flows to the forwarder. However, due to the churn in heavy hitter flows, the real challenge in employing this approach is how to select the set of heavy hitter flows that minimize the churn. To overcome this problem, this paper proposed a heavy hitter selection strategy – Traffic-aware Flow Offloading (TFO). TFO keeps tracking traffic statistics at multiple time scales and uses it in the heavy hitter selection process in order to maintain a high offloading ratio while limiting the changes to the set of heavy hitters. The paper used simulation on real traffic traces to evaluate TFO and compared it with traditional caching and bin-optimal. The results suggested that TFO can achieve similar offloading effectiveness as the bin-optimal, but with an order of magnitude smaller churn ratio. The paper also showed that TFO outperforms LRU and LFU in terms of the churn ratio when a small number of heavy hitters are selected. Though the core idea presented in this paper is simple, I find this paper interesting because the paper took one step further beyond simply exploring the Zipf's property of the Internet traffic and focused on solving the real challenge in selecting heavy hitters with limited churn ratio. The preliminary results show that TFO can be a promising solution. Clearly there are many interesting tradeoffs involved in TFO that need careful evaluation. For example, the parameters used in TFO are not thoroughly evaluated. In addition, it will be useful to show how TFO performs under more diverse set of traffic load and behavior patterns. Overall, the paper has presented an interesting idea and is well written. A more thorough evaluation of TFO will further strengthen the paper.