Invited Speaker 4: Neeraja J. Yadwadkar

Traditional resource management techniques that rely on simple heuristics often fail to achieve predictable performance in contemporary complex systems that span physical servers, virtual servers, private and/or public clouds. My research aims to bring the benefits of Machine Learning (ML) models to optimize and manage such complex systems by deriving actionable insights from the performance and utilization data these systems generate. To realize this vision of model-based resource management, we need to deal with the following key challenges data-driven ML models raise: uncertainty in predictions, cost of training, generalizability from benchmark datasets to real-world systems datasets, and interpretability of the models.

In this talk, I will present our the ML formulations to demonstrate how to handle these challenges for two main problem domains in distributed systems: (I) Scheduling in parallel data-intensive computational frameworks for improved tail latencies, and (II) Performance-aware resource allocation in the public cloud environments for meeting user-specified performance and cost goals. Along the way, I will also share a list of guidelines for leveraging ML for solving problems in systems, based on my experience.