Duration: 17:02

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Speakers:
Venkat Padmanabhan, Deputy Managing Director, Microsoft Research India
Zhixiong Niu, Senior Researcher, Microsoft Research Asia
Francis Y. Yan, Senior Researcher, Microsoft Research and Azure

Today, applications deployed in the cloud or at the edge provide services to end users over a heterogeneous array of networks, from high-speed wired links to fragile wireless communications. Data-driven techniques, including machine learning (ML), can enable the realistic evaluation of networked applications using simulators and emulators, and may equip such applications with better adaptability and versatility in these diverse scenarios. However, achieving good performance in practice demands thoughtfully designed ML algorithms as well as diverse learning environments.

Join us for a discussion on this timely topic with Microsoft researchers Francis Y. Yan and Zhixiong Niu, moderated by Venkat Padmanabhan. During this discussion, they will introduce: OpenNetLab, an open distributed platform of heterogeneous nodes, established to promote data-driven networking research, and the iBox (Internet on a Box) data-driven simulator; MMSys '21 grand challenge on bandwidth estimation for real-time video, showcasing OpenNetLab's capability to foster ML-based network algorithms; and a related cross-lab research project which aims to leverage reinforcement learning to optimize quality of experience for videoconferencing users.

Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit