RINGS: Resilient Edge Networks with Data-driven Model-based Learning

具有嵌入式计算能力和服务的下一代蜂窝无线边缘网络已成为关键基础设施，可在教育、商业、交通、医疗保健和娱乐等许多领域实现安全、稳健和高性能的应用。同时，边缘网络的可用性、可靠性和弹性不断受到预期资源和需求变化的挑战，例如，昼夜应用业务模式、用户移动性和随机链路/节点故障，以及操作条件的意外电平偏移，例如，突发交通事件-由突发事件、恶意攻击和自然灾害引发的重大基础设施故障引发的人群。 该项目从通信和计算资源的联合供应、分配和调度的角度，为自主和弹性无线边缘网络开发新型混合学习解决方案。它包括几个研究重点：1）为了在面对预期的需求/资源变化时实现高效率和鲁棒性，项目团队正在研究长时间尺度上的鲁棒通信和计算资源供应。在短时间尺度上，该团队正在研究模型预测控制框架下的自适应路由与自适应动态规划（ADP）。研究人员还在研究使用领域知识丰富的强化学习框架对虚拟中间盒进行自适应调度。2)为了从重大中断中恢复，项目团队正在研究如何通过战略性地利用备份和外部资源逐步恢复网络服务。通过基于混合在线学习的联合配置、路由和调度实现弹性渐进式恢复。在Robust ADP框架下分析了恢复过程的稳定性。3)正在使用数学分析，跟踪驱动的模拟和CloudLab测试平台上的实验来评估各个研究组件。该项目正在产生关于混合学习的新理论和算法，可用于研究其他领域的复杂系统。该奖项反映了NSF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。

Next-generation cellular wireless edge networks with embedded computational capabilities and services have become a critical infrastructure that enables secure, robust, and high-performance applications in many domains, including education, business, transportation, healthcare, and entertainment. Meanwhile, the availability, reliability, and resiliency of edge networks are constantly being challenged by both the expected resource and demand variations, e.g., diurnal application traffic patterns, user mobility, and random link/node failures, as well as the unexpected level-shifts of operating conditions, e.g., traffic flash-crowds triggered by emerging events, major infrastructure failures after coordinated malicious attacks and natural disasters. The project is developing novel hybrid learning solutions for autonomous and resilient wireless edge networks from the angle of joint provisioning, allocation, and scheduling of communication and computation resources. It includes several research thrusts: 1) To achieve high efficiency and robustness in the face of expected demand/resource variations, the project team is investigating robust communication and computation resource provisioning at long time scales. At short time scales, the team is studying adaptive routing under the Model Predictive Control framework coupled with Adaptive Dynamic Programming (ADP). The researchers are also investigating adaptive scheduling of virtual middle-boxes using a domain-knowledge enriched reinforcement learning framework. 2) To recover from major disruptions, the project team is studying how to progressively bring network services back by strategically utilizing backup and external resources. Resilient progressive recovery is achieved through joint provisioning, routing, and scheduling based on hybrid online learning. The stability of the recovery process is analyzed under the Robust ADP framework. 3) Individual research components are being evaluated using mathematical analysis, trace-driven simulations, and experiments on the CloudLab testbed. The project is generating new theories and algorithms on hybrid learning that can be used to study complex systems in other domains. Valuable research opportunities are being created for graduate and undergraduate students, especially women and minority students.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.