CAREER: Data-Driven Network Resource Management Systems

职业：数据驱动的网络资源管理系统

• 批准号: 1751009
• 负责人: Mohammad Alizadeh
• 金额: $ 62.8万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751009&HistoricalAwards=false
• 关键词: CAREER; Data; Driven; Network; Resource

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.

现代网络需要复杂的系统和算法来有效地管理资源，并向用户提供高质量的经验。这些系统对社会的服务至关重要，从视频流到社交网络再到AI应用程序。例如，视频流涉及许多系统，这些系统可以控制从视频的分辨率到网络路径的分辨率以及基于动态网络条件的视频下载速度。随着网络和应用程序变得越来越复杂，现有的方法变得不足并设计在所有条件下提供高性能的算法变得非常困难。这项研究的目的是通过开发网络系统来解决这一挑战，这些网络系统通过应用新的机器学习技术来自动管理资源。如果成功的话，这个新的范式将使网络更容易设计，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用于设计资源管理系统的算法和系统基础，这些系统使用现代强化学习和其他预测控制技术，以在异质网络和应用程序之间实现强大的性能。为此，研究人员计划为重要应用程序构建一系列实用系统，包括集群计算系统的调度程序（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360虚拟现实视频的自适应流）。在构建这些系统时，研究人员将应对面对数据驱动的网络资源管理的基本挑战，包括（i）代表工作负载（例如图形结构化的作业）和网络（例如拓扑，排队，排队，流动，流动）的技术，以促进使用神经网络学习； （ii）处理具有较大和深度动作空间的挑战性资源管理问题的技术； （iii）有效收集跨多种学习控制模型的设备的数据； （iv）在部署奖后，从离线数据收集的数据中引导数据模型的技术不断训练模型，反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。

项目成果

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

• 发表时间: 2019-06
• 作者: Ravichandra Addanki;S. Venkatakrishnan;Shreyan Gupta;Hongzi Mao;Mohammad Alizadeh

Real-Time Video Inference on Edge Devices via Adaptive Model Streaming

• DOI: 10.1109/iccv48922.2021.00453
• 发表时间: 2020-06
• 期刊: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
• 作者: Mehrdad Khani Shirkoohi;Pouya Hamadanian;Arash Nasr-Esfahany;Mohammad Alizadeh

RECL: Responsive Resource-Efficient Continuous Learning for Video Analytics

RECL：视频分析的响应式资源高效持续学习

• 发表时间: 2023
• 期刊: 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23
• 作者: Khani, Mehrdad;Ananthanarayanan, Ganesh;Hsieh, Kevin;Jiang, Junchen;Netravali, Ravi;Shu, Yuanchao;Alizadeh, Mohammad;Bahl, Victor
• 通讯作者: Bahl, Victor

Adaptive Neural Signal Detection for Massive MIMO

• DOI: 10.1109/twc.2020.2996144
• 发表时间: 2020-08-01
• 期刊: IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
• 影响因子: 10.4
• 作者: Khani, Mehrdad;Alizadeh, Mohammad;Fleming, Phil
• 通讯作者: Fleming, Phil

Robust Query Driven Cardinality Estimation under Changing Workloads

不断变化的工作负载下鲁棒查询驱动的基数估计

• DOI: 10.14778/3583140.3583164
• 发表时间: 2023
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Negi, Parimarjan;Wu, Ziniu;Kipf, Andreas;Tatbul, Nesime;Marcus, Ryan;Madden, Sam;Kraska, Tim;Alizadeh, Mohammad
• 通讯作者: Alizadeh, Mohammad