Adversarial Design Framework for Self-Driving Networks (ADVISE)

自动驾驶网络的对抗性设计框架（ADVISE）

• 批准号: 438892507
• 负责人: Professor Dr.-Ing. Wolfgang Kellerer, since 4/2022
• 金额: --
• 依托单位: Lehrstuhl für Kommunikationsnetze (LKN)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/438892507?language=en
• 关键词: Adversarial; Design; Framework; Self; Driving

The networking community is currently engaged in designing more automated and ``self-driving'' communication networks that overcome today's manually managed networks. These networks exploit the flexibilities introduced by emerging software-defined and virtualized communication technologies, to implement more demand-aware networks which meet the stringent requirements of new applications arising, e.g., in the context of 5G applications, such as low-latency tele-operation or high-bandwidth machine-to-machine type communication.This project proposes a network design framework relying on new methodologies to realize the vision of such self-driving networks, by combining adapted machine learning and artificial intelligence with approaches providing formal correctness and performance guarantees. We consider the lack of rigorous guarantees by existing algorithms based on artificial intelligence as one of the key issues which can prevent the adoption of self-driving networks: communication networks have become a critical infrastructure of our digital society and hence need to provide dependability and deterministic performance guarantees. In particular, we consider adversarial and game-theoretic approaches to test and optimize networks, to leverage the performance benefits from machine learning approaches while at the same time provide rigorous worst-case guarantees.The PIs are well-prepared for this project, through the unique combination of expertise in machine learning and network algorithms, as also demonstrated in their recent joint publications leading to this project.

网络界目前正在设计更自动化和"自动驾驶“的通信网络，以克服当今人工管理的网络。这些网络利用了新兴的软件定义和虚拟化通信技术所引入的灵活性，以实现更多的需求感知网络，这些网络满足新应用的严格要求，例如，该项目提出了一个网络设计框架，该框架依赖于新的方法，通过将自适应机器学习和人工智能与提供形式正确性和性能保证的方法相结合，来实现这种自动驾驶网络的愿景。我们认为，现有的基于人工智能的算法缺乏严格的保证是阻碍自动驾驶网络采用的关键问题之一：通信网络已成为我们数字社会的关键基础设施，因此需要提供可靠性和确定性的性能保证。特别是，我们考虑了对抗和博弈论方法来测试和优化网络，以利用机器学习方法的性能优势，同时提供严格的最坏情况保证。PI通过机器学习和网络算法专业知识的独特组合，为这个项目做好了充分的准备，这也体现在他们最近的联合出版物中。