A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time, but remain to be irreconcilable. High resource utilization increases the risk of resource contention between colocated workload, which makes latencycritical (LC) applications suffer unpredictable, and even unacceptable performance. Plenty of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource efficiency. In this paper, we propose MAGI, a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference, and adjusts resource shares of corresponding applications to ensure the QoS of LC applications. MAGI is a practice in Alibaba datacenter to provide ondemand resource adjustment for applications using neural networks. The experimental results show that MAGI could reduce up to 87.3% performance degradation of LC application when colocated with other antagonist applications.

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.