Proceedings of the ninth ACM conference on Emerging networking experiments and technologies
Scalable, optimal flow routing in datacenters via local link balancing
Proceedings of the ninth ACM conference on Emerging networking experiments and technologies
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Data center networks typically have many paths connecting each host pair to achieve high bisection bandwidth for arbitrary communication patterns. Fully utilizing the bisection bandwidth may require flows between the same source and destination pair to take different paths. However, existing routing protocols have little support for load-sensitive adaptive routing. We propose DARD, a Distributed Adaptive Routing architecture for Data center networks. DARD allows each end host to move traffic from overloaded paths to under loaded paths without central coordination. We use an Open Flow implementation and simulations to show that DARD can effectively use a data center network's bisection bandwidth under both static and dynamic traffic patterns. It outperforms previous solutions based on random path selection by 10%, and performs similarly to previous work that assigns flows to paths using a centralized controller. We use competitive game theory to show that DARD's path selection algorithm makes progress in every step and converges to a Nash equilibrium in finite steps. Our evaluation results suggest that DARD can achieve a close-to-optimal solution in practice.