informationAgnostic Traffic Scheduling in Data Center Networks with Asymmetric Topologies As more and more applications are deployed in data centers, they rely on high performance data center networks (DCNs) to meet usersxe2x80x99 increasing quality of the experience (QoE) requirements. Hence, minimizing the average flow completion time (FCT) has been one of the most important goals for DCNs. However, existing traffic scheduling methods assume either prior knowledge of flows (i.e., sizes and deadlines) or symmetric topologies (i.e., FatTree or Bcube). In practice, it is difficult to obtain the information of flows. Moreover, even with symmetric topology design, the DCNs will become asymmetric due to the inevitable link failure and congestion. In this case, it is a great challenge to minimize the average FCT in DCNs. In this paper, we propose a flowlet based informationagnostic traffic scheduling mechanism. The key idea of our method is leveraging multiple priority queues in switches to demote the priority of flows dynamically on the flowlet level. More specifically, the priority of a flow will be demoted according to the number of flowlets it has sent, which follows the shortest job first discipline. We formulate the average FCT minimization problem as a nonlinear SumofRatios problem and design two heuristic methods to derive the suboptimal demotion thresholds. Experiment results show that our method can reduce the average FCT by up to 15.35% with a realistic workload, as compared to the stateoftheart traffic scheduling methods.

Machine Learning Assisted Optical Network Resource Scheduling in Data Center Networks Parallel computing allows us to process incredible amounts of data in a timely manner by distributing the workload across multiple nodes and executing computation simultaneously. However, the performance of this parallelism usually suffers from network bottleneck. In optical switching enabled data center networks (DCNs), to satisfy the complex and timevarying bandwidth demands from the parallel computing, it is critical to fully exploit the flexibility of optical networks and meanwhile reasonably schedule the optical resources. Considering that the traffic flows generated by different applications in DCNs usually exhibit different statistical or correlative features, it is promising to schedule the optical resources with the assistance of machine learning. In this paper, we introduce a framework called intelligent optical resources scheduling system, and discuss how this framework can assist resource scheduling based on machine learning approaches. We also present our recent simulation results to verify the performance of the framework.