AI

Efficient Traffic Splitting on Commodity Switches

Abstract

Traffic often needs to be split over multiple equivalent backend servers, links, paths, or middleboxes. For example, in a load-balancing system, switches distribute requests of online services to backend servers. Hash-based approaches like Equal-Cost Multi-Path (ECMP) have low accuracy due to hash collision and incur significant churn during update. In a Software-Defined Network (SDN) the accuracy of traffic splits can be improved by crafting a set of wildcard rules for switches that better match the actual traffic distribution. The drawback of existing SDN-based traffic-splitting solutions is poor scalability as they generate too many rules for small rule-tables on switches. In this paper, we propose Niagara, an SDN-based traffic-splitting scheme that achieves accurate traffic splits while being extremely efficient in the use of rule-table space available on commodity switches. Niagara uses an incremental update strategy to minimize the traffic churn given an update. Experiments demonstrate that Niagara (1) achieves nearly optimal accuracy using only 1.2%−37% of the rule space of the current state-of-art, (2) scales to tens of thousands of services with the constrained rule-table capacity and (3) offers nearly minimum churn.