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Neural network robustness has become a central topic in machine learning in recent years. Most training algorithms that improve the model’s robustness to adversarial and common corruptions also introduce a large computational overhead, requiring as many as ten times the number of forward and backward passes in order to converge. To combat this inefficiency, we propose BulletTrain — a boundary example mining technique to drastically reduce the computational cost of robust training. Our key observation is that only a small fraction of examples are beneficial for improving robustness. BulletTrain dynamically predicts these important examples and optimizes robust training algorithms to focus on the important examples. We apply our technique to several existing robust training algorithms and achieve a 2.2× speed-up for TRADES and MART on CIFAR-10 and a 1.7× speed-up for AugMix on CIFAR-10-C and CIFAR-100-C without any reduction in clean and robust accuracy. 

Cloud applications are increasingly shifting to interactive and loosely-coupled microservices. Despite their advantages, microservices complicate resource management, due to inter-tier dependencies. We present Sinan and PuppetMaster, two cluster managers for interactive microservices that leverages easily-obtainable tracing data instead of empirical decisions, to infer the impact of a resource allocation on end-to-end performance, and allocate appropriate resources to each tier. In a preliminary evaluation of the system with an end-to-end social network built with microservices, we show that the cluster manager's data-driven approach allows the service to always meet its QoS without sacrificing resource efficiency. 

Cloud applications are increasingly shifting to interactive and loosely-coupled microservices. Despite their advantages, microservices complicate resource management, due to inter-tier dependencies. We present Sinan, a cluster manager for interactive microservices that leverages easily-obtainable tracing data instead of empirical decisions, to infer the impact of a resource allocation on end-to-end performance, and allocate appropriate resources to each tier. In a preliminary evaluation of Sinan with an end-to-end social network built with microservices, we show that Sinan’s data-driven approach, allows the service to always meet its QoS without sacrificing resource efficiency.