Learning Oriented Computer Networks

面向学习的计算机网络

• 批准号: RGPIN-2021-03851
• 负责人: Pan, Jianping
• 金额: $ 4.66万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749887
• 关键词: Learning; Oriented; Computer; Networks

The "data pipe" model used by the existing Internet protocol stack is no longer adequate for many emerging applications. Information, which can have different data representations, and knowledge, e.g., learned model parameters, have communication patterns and requirements different from those of point-to-point, reliable and elastic data transfer. Information and knowledge can be aggregated and transformed inside the network, due to latency constraints, and control scope is no longer limited to endpoint-to-endpoint only. On the other hand, new communication infrastructures such as 6G mobile communication systems, low-orbit satellites and underwater/ground communication systems are emerging rapidly, which offer additional communication capabilities with certain topological features. Now the network protocol stack between application requirements and communication services becomes a new bottleneck. This research program focuses on a new computer network architecture serving learning-centric applications in data centers, around network edges and on mobile devices, by leveraging, improving and creating new learning techniques to determine and optimize protocol mechanisms and control policies. It will further our research in the last two decades on topology control, protocol design and performance evaluation for computer networks, to network a truly dependable cyber-infrastructure. The learning-oriented computer network can learn from applications and communications automatically and continuously while running on different infrastructures to support diverse requirements, and keep evolving its protocol mechanisms and control policies in an online manner, while maintaining security and preserving privacy, to learn and perform more effectively and efficiently. Specifically, using decomposition, protocol stack can be decomposed into functional building blocks to reduce redundancy and repetition in the current layered protocol stack. The learning and sequential decision-making algorithms can compose certain building blocks for a given application and configure their operating parameters automatically based on the learned application requirements and network dynamics. This new approach can fundamentally change the existing approach of limited functionality and performance in the current, fixed protocol stack, and open new space for future applications. On the other hand, the modular computer network architecture will support in-network information processing with computing, storage and control for decentralized, federated and distributed learning applications. Through this research program, a diverse and inclusive team of undergrad and graduate students, postdoctoral fellows and industry personnel will be trained and worked with, and the research outcomes can directly benefit the Canadian society and economy by leveraging the Canadian leadership in artificial intelligence to meet the need for dependable, high-performance and low-cost cyber-infrastructures.

现有的互联网协议栈所使用的“数据管道”模型不再适用于许多新兴应用。可以具有不同数据表示的信息和知识(例如，学习的模型参数)具有不同于点对点、可靠和弹性数据传输的通信模式和要求。由于延迟限制，信息和知识可以在网络内聚合和转换，控制范围不再仅限于端到端。另一方面，6G移动通信系统、低轨卫星和水下/地面通信系统等新的通信基础设施正在迅速涌现，这些基础设施提供了具有一定拓扑特征的额外通信能力。现在，应用需求和通信服务之间的网络协议栈成为新的瓶颈。这项研究计划专注于一种新的计算机网络体系结构，通过利用、改进和创造新的学习技术来确定和优化协议机制和控制策略，从而为数据中心、网络边缘和移动设备上以学习为中心的应用程序提供服务。它将推动我们在过去二十年中对计算机网络的拓扑控制、协议设计和性能评估的研究，以建立一个真正可靠的网络基础设施。学习型计算机网络能够在不同的基础设施上运行时，自动、连续地从应用和通信中学习，以支持多样化的需求，并以在线的方式不断演化其协议机制和控制策略，同时维护安全和保护隐私，以更有效和高效地学习和执行。具体地说，利用分解，可以将协议栈分解成功能构建块，以减少当前分层协议栈中的冗余和重复。学习和顺序决策算法可以为给定的应用组成特定的构建块，并基于所学习的应用需求和网络动态自动配置其操作参数。这种新方法可以从根本上改变当前固定协议堆栈中功能和性能有限的现有方法，并为未来的应用打开新的空间。另一方面，模块化计算机网络结构将支持网内信息处理，为分散的、联合的和分布式的学习应用程序提供计算、存储和控制。通过这一研究计划，一个由本科生和研究生、博士后研究员和行业人员组成的多元化和包容性团队将得到培训和合作，研究成果可以通过利用加拿大在人工智能领域的领先地位来满足对可靠、高性能和低成本网络基础设施的需求，从而直接造福加拿大社会和经济。