CAREER: Data-Driven Network Resource Management Systems

现代网络需要复杂的系统和算法来高效地管理资源并为用户提供高质量的体验。这些系统对于社会已经开始依赖的服务至关重要，从视频流到社交网络再到人工智能应用。例如，视频流涉及许多系统，这些系统根据动态网络条件控制从视频分辨率到网络路径和视频下载速度的一切。随着网络和应用变得越来越复杂，现有的方法已经变得不够用，设计在所有条件下都能提供高性能的算法变得极其困难。这项研究的目标是通过开发网络系统来应对这一挑战，该系统通过应用新的机器学习技术来学习通过经验自动管理资源。这一新模式如果成功，将使网络更易于设计、更高效、更具成本效益，并能够为企业和消费者提供更好的服务。该项目的目标是开发算法和系统基础，以设计使用现代强化学习和其他预测控制技术的资源管理系统，以在不同的网络和应用程序中实现强大的性能。为此，研究人员计划为重要应用构建一系列实用系统，包括用于集群计算系统的调度器(例如，用于数据并行分析工作负载)和上下文感知网络控制协议(例如，用于自适应流传输360虚拟现实视频)。在构建这些系统时，研究人员将解决数据驱动的网络资源管理面临的基本挑战，包括(I)表示工作量(例如，图结构作业)和网络(例如，拓扑、队列、流)的技术，以促进使用神经网络进行学习；(Ii)利用大而深的动作空间处理具有挑战性的资源管理问题的技术；(Iii)跨无数设备高效地收集数据以学习控制模型的技术；以及(Iv)从离线收集的数据中引导学习模型并在部署后持续安全地培训模型的技术该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Modern networks require sophisticated systems and algorithms to manage resources efficiently and deliver high quality of experience to users. These systems are critical to services society has come to rely on, from video streaming to social networks to AI applications. Video streaming, for example, involves numerous systems that control everything from the resolution of the video to the network path and the video download speed based on dynamic network conditions. As networks and applications have become more complex, existing approaches have become inadequate and designing algorithms that deliver high performance in all conditions has become exceedingly difficult. The goal of this research is to address this challenge by developing network systems that learn to manage resources automatically through experience by applying new machine learning techniques. This new paradigm, if successful, will make networks simpler to design, more efficient and cost effective, and able to deliver better services to businesses and consumers. This project's goal is to develop the algorithmic and systems foundations for designing resource management systems that use modern reinforcement learning and other predictive control techniques to achieve strong performance across heterogeneous networks and applications. To this end, the researchers plan to build a series of practical systems for important applications, including schedulers for cluster computing systems (e.g., for data-parallel analytics workloads), and context-aware network control protocols (e.g., for adaptive streaming of 360 virtual reality video). In building these systems, the researchers will tackle fundamental challenges that confront data-driven network resource management, including (i) techniques to represent workloads (e.g., graph-structured jobs) and networks (e.g., topologies, queues, flows) to facilitate learning using neural networks; (ii) techniques to handle challenging resource management problems with large and deep action spaces; (iii) techniques to efficiently collect data across a myriad of devices for learning control models; and (iv) techniques to bootstrap learning models from data collected offline and continually train models safely after deploymentThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.