Abstract

With the fast growth of cloud services and network scales, the heavy and highly dynamic traffic demands pose great challenges to the efficient traffic engineering in today's data center networks (DCNs) [21]. The DCN flows can be broadly classified into two main categories: delay-sensitive small flows (e.g., queries or realtime small messages) and throughput-sensitive large flows (e.g., the backup traffic). In general, more than 80% flows in data centers are small flows, while the majority of the traffic volume is contributed by the top 10% large flows [3, 7]. To handle the mixed traffic, today's data centers [1, 14] generally follow the tree-based topologies (e.g., fat-tree) and take the load-agnostic routing strategies based on random path selection (e.g., ECMP1) [14, 19]. Although it is applicable for routing small flows which are highly random, these strategies are likely to route several large flows through the same output link and lead to long-lived congestions [2, 8]. With the limited switch buffer occupied by large flows for a long time, small flows are reported to experience one order of magnitude larger delay, which compromises the performance of DCNs and makes the users suffer [3].

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