CNS Core: Medium: Collaborative: Exploring and Exploiting Learning for Efficient Network Control: Non-Stationarity, Inter-Dependence, and Domain-Knowledge

传统上，计算机网络协议和控制机制是按照一定的理论模型或设计原则设计和工程的，在（通常简化的）关于它们运行的网络环境的假设下。网络操作大多是由操作员通过手动配置控制参数和资源来完成的，有时还会以测量分析和性能优化为指导。随着应用范围的日益广泛和网络场景的日益复杂，传统的方法并不总是能很好地发挥作用。为了应对这一挑战，机器学习（ML）技术已被应用于广泛的网络和分布式系统问题，从降低数据中心冷却成本到流量优化和应用程序管理。虽然初步结果很有希望，但将机器学习技术应用于网络提出了许多重要的研究问题，必须进行系统和深入的探索。提出的研究从理论和实践的角度为基于学习的网络控制提供了原则性理解的重要第一步，并为基于学习的网络控制提供了新的机会。它将有助于推动自动驾驶网络和人工智能It运营的新兴愿景，并为网络运营商，用户和社会带来好处。该项目还将研究与教育结合起来，扩大计算机领域的参与，特别是招募和培训女性和代表性不足的学生，以及向K-12开展推广活动。网络是控制和（分布式）数据平面元素的集合，它们在不同的时间尺度上对不同类型的数据进行操作，响应和适应流量需求和网络状态的变化，以实现不同的目标。网络环境具有高度的动态性和不确定性，由于流量需求的激增和时间变化而产生的非平稳性，以及不可预测的网络故障；它们也具有内在的关联、相互依赖和约束，部分原因是各种网络实体之间复杂的相互作用。此外，网络是工程系统——有一些基本原则支配着它们的设计和操作，具有不可违反的约束和内在关系，可以产生实质性的性能增益。提出的研究重点是基于学习的网络控制问题，以解决这些挑战以及以下相互关联的研究重点。在推力1，以网络为中心的学习技术中，该项目将探索基本限制（从理论角度），并为非平稳、相关和约束环境推进新的以网络为中心的机器学习技术。在推力2中，基于学习的全网络控制和水平/垂直交互，该项目将通过利用（水平和垂直）交互和利用共享学习，在全网络框架中研究和开发创新的基于学习的网络控制算法。最后但并非最不重要的是，在评估推力中，该项目将评估提出的基于学习的网络控制算法，并将其与传统优化和其他基于ML的方法进行比较。项目资料，如出版物、开发的算法、收集的数据和人员，将在整个项目期间和项目完成后的五年内在https://web.cs.ucdavis.edu/~liu/Research/Holistic.htm上公开提供。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Traditionally, computer network protocols and control mechanisms are designed and engineered in accordance with certain theoretical models or design principles, under (often simplifying) assumptions about the network environment in which they operate. Network operations are mostly performed by operators through manual configurations of control parameters and resources, sometimes guided by measurement analysis and performance optimization. With the increasingly wide range of applications and complex network scenarios, traditional methods do not always perform well. To address this challenge, machine learning (ML) techniques have been applied to a wide range of networking and distributed systems problems, from reducing data center cooling costs to traffic optimization and application management. While preliminary results are promising, applying machine learning techniques to networking pose many important research questions that must be explored systematically and in depth. The proposed research constitutes an important first step toward providing a principled understanding of the fundamental limitations and promising new opportunities in learning-based network control from both theoretical and practical perspectives. It will help advance the emerging visions of self-driving networks and AIOps (Artificial Intelligence for IT Operations), and bring benefits to network operators, users, and the society at large. This project also integrates research with education and broadens participation in computing, especially with recruitment and training of female and under-represented students and outreach activities to K-12. Networks are a collection of control and (distributed) data plane elements that operate at different time scales on diverse types of data, respond and adapt to changes in traffic demands and the network state to achieve disparate objectives. The networking environments are highly dynamic and uncertain, with non-stationarity caused by surges and time-of-day changes in traffic demands, and unpredictable network failures; they are also inherently correlated, inter-dependent and constrained, in part due to complex interactions of various network entities. Moreover, networks are engineered systems -- there are basic principles that govern their designs and operations, with constraints that cannot be violated and inherent relations that could yield substantial performance gains. The proposed research focuses on learning-based network control problems to address these challenges along the following inter-related research thrusts. In Thrust 1, Network-Centric Learning Techniques, this project will explore the fundamental limits (from a theoretical perspective) and advance new network-centric ML techniques for non-stationary, correlated and constrained environments. In Thrust 2, Network-wide Learning-based Control and Horizontal/Vertical Interactions, this project will study and develop innovative learning-based network control algorithms in a network-wide framework by exploiting the (horizontal and vertical) interactions and leveraging shared learning. Last but not the least, in the Evaluation Thrust, this project will evaluate the proposed learning-based network control algorithms and compare them with conventional optimization and other ML based approaches.The project information such as publications, algorithms developed, data collected and personnel, will be made publicly available at https://web.cs.ucdavis.edu/~liu/Research/Holistic.htm during the entire project duration and for five years after the completion of this project.This 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.