The Quality of Service (QoS) of scheduling between latency-sensitive small data flows (a.k.a. mice) and throughput-oriented large ones (a.k.a. elephants) has become ever challenging with the proliferation of cloud-based applications. In light of this mounting problem, this work proposes a novel flow control scheme, HOLMES (HOListic Mice-Elephants Stochastic), which offers a holistic view of global congestion awareness as well as a stochastic scheduler of mixed mice-elephants data flows in Data

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... enter Networks (DCNs). Firstly, we theoretically prove the necessity for partitioning DCN paths into sub-networks using a stochastic model. Secondly, the HOLMES architecture is proposed, which adaptively partitions the available DCN paths into low-latency and high-throughput sub-networks via a global congestion-aware scheduling mechanism. Based on the stochastic power-of-two-choices policy, the HOLMES scheduling mechanism acquires only a subset of the global congestion information, while achieves close to optimal load balance on each end-to-end DCN path. We also formally prove the stability of HOLMES flow scheduling algorithm. Thirdly, extensive simulation validates the effectiveness and dependability of HOLMES with select DCN topologies. The proposal has been in test in an industrial production environment. An extensive survey of related work is also presented.