Distributed adaptive routing for big-data applications running on data center networks

With the growing popularity of big-data applications, Data Center Networks increasingly carry larger and longer traffic flows. As a result of this increased flow granularity, static routing cannot efficiently load-balance traffic, resulting in an increased network contention and a reduced throughput. Unfortunately, while adaptive routing can solve this load-balancing problem, network designers refrain from using it, because it also creates out-of-order packet delivery that can significantly degrade the reliable transport performance of the longer flows. In this paper, we show that by throttling each flow bandwidth to half of the network link capacity, a distributed-adaptive-routing algorithm is able to converge to a non-blocking routing assignment within a few iterations, causing minimal out-of-order packet delivery. We present a Markov chain model fo