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

CNS 核心：中：协作：探索和利用学习实现高效网络控制：非平稳性、相互依赖和领域知识

• 批准号: 1901103
• 负责人: Zhi-Li Zhang
• 金额: $ 33.13万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901103&HistoricalAwards=false
• 关键词: CNS; Core; Medium; Collaborative; Exploring

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.

传统上，计算机网络协议和控制机制是根据某些理论模型或设计原则来设计和设计的，这些理论模型或设计原则是在(通常是简化的)关于它们运行的网络环境的假设下进行的。网络操作主要由运营商通过手动配置控制参数和资源来执行，有时还会以测量分析和性能优化为指导。随着应用的日益广泛和复杂的网络场景，传统的方法并不总是表现良好。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管理。虽然初步结果是有希望的，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，必须系统和深入地探索。这项拟议的研究是从理论和实践两个角度对基于学习的网络控制的基本局限性和有希望的新机会进行原则性理解的重要的第一步。它将有助于推动自动驾驶网络和AIOps(人工智能)的新兴愿景，并为网络运营商、用户和整个社会带来好处。该项目还将研究与教育结合起来，扩大对计算机的参与，特别是招募和培训女学生和任职人数不足的学生，并向12岁以下儿童开展外联活动。网络是控制和(分布式)数据平面元素的集合，它们以不同的时间尺度对不同类型的数据进行操作，响应并适应流量需求和网络状态的变化，以实现不同的目标。网络环境是高度动态和不确定的，流量需求的激增和时间变化以及不可预测的网络故障造成了非平稳性；它们还内在地相互关联、相互依赖和约束，部分原因是各种网络实体的复杂交互。此外，网络是工程化的系统--有管理其设计和操作的基本原则，有不能违反的限制和可以产生实质性性能提升的内在关系。建议的研究重点是基于学习的网络控制问题，以解决这些挑战，沿着以下相互关联的研究主线。在主题1，以网络为中心的学习技术，这个项目将探索基本的限制(从理论的角度)，并为非静态、相关和受限的环境提出新的以网络为中心的学习技术。在推力2，基于网络的学习控制和水平/垂直互动中，该项目将通过利用(水平和垂直)互动和利用共享学习，在网络范围的框架内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，本项目将评估建议的基于学习的网络控制算法，并将它们与传统的优化和其他基于ML的方法进行比较。项目信息，如出版物、开发的算法、收集的数据和人员，将在整个项目持续时间和项目完成后的五年内在https://web.cs.ucdavis.edu/~liu/Research/Holistic.htm上公开。该奖项反映了国家科学基金会的法定使命，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。

项目成果

Kaala: scalable, end-to-end, IoT system simulator

Kaala：可扩展、端到端的物联网系统模拟器

• DOI: 10.1145/3538393.3544937
• 发表时间: 2022
• 期刊: SIGCOMM workshop on networked sensing systems for a sustainable society
• 作者: Dayalan, Udhaya Kumar;Fezeu, Rostand A.;Salo, Timothy J.;Zhang, Zhi-Li
• 通讯作者: Zhang, Zhi-Li

Domain Disentangled Meta-Learning

• DOI: 10.1137/1.9781611977653.ch61
• 发表时间: 2023
• 作者: Xin Zhang;Yanhua Li;Ziming Zhang;Zhi-Li Zhang

Making content caching policies 'smart' using the deepcache framework

• DOI: 10.1145/3310165.3310174
• 发表时间: 2019-01
• 期刊: Comput. Commun. Rev.
• 作者: A. Narayanan;Saurabh Verma;Eman Ramadan;Pariya Babaie;Zhi-Li Zhang

Raven: belady-guided, predictive (deep) learning for in-memory and content caching

• DOI: 10.1145/3555050.3569134
• 发表时间: 2022-11
• 期刊: Proceedings of the 18th International Conference on emerging Networking EXperiments and Technologies
• 作者: Xinyue Hu;Eman Ramadan;Wei Ye;Feng Tian;Zhi-Li Zhang

COREL: Constrained Reinforcement Learning for Video Streaming ABR Algorithm Design Over mmWave 5G

• DOI: 10.1109/cqr59928.2023.10317803
• 发表时间: 2023-10
• 期刊: 2023 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR)
• 作者: Xinyue Hu;Arnob Ghosh;Xin Liu;Zhi-Li Zhang;N. Shroff