Autonomous maintenance for satellite-terrestrial integrated networks

在疫情期间和之后的世界，可靠的宽带互联网在我们的日常生活中从未如此不可或缺。然而，仅在2021年，全国范围内的互联网中断就发生了几次。4月的罗杰斯网络中断和10月的社交媒体服务中断造成了数十亿美元的损失。未来的互联网将面临严峻的挑战。一方面，据国际电信联盟报告，预计它将为目前没有宽带的全球85%以上的人口提供全球宽带接入。另一方面，随着全球空间和地面电信基础设施日益复杂，需要确保高弹性的互联网接入。随着大规模非地球静止卫星（NGSO）的新发射计划，未来的互联网基础设施将基于一种称为卫星-地面综合网络（STIN）的新范式，支持卫星和地面网络的融合。预计STIN将通过将先进的中地球轨道（MEO）和低地球轨道（LEO）卫星星座集成到异构地面段中，实现全球宽带互联网。然而，如果没有有效的维护和操作来保证弹性，这一愿景很难实现。例如，任何网段上的故障组件、大气事件或异常流量都很容易降低或中断网络服务。空间和地面组件的复杂性、异构性和动态性带来了持续的操作挑战。为STINs配备自主维护（AM）功能，是应对这些挑战的理想解决方案。这样的解决方案可以处理不断增加的stin复杂性并减轻性能下降。提出的研究的长期目标是开发新的AM方法，使基于stin的互联网基础设施具有弹性，智能和自我维护。短期目标是为基于ngso卫星的STIN基础设施探索AM在根本原因分析和异常缓解中的基本要素。拟议的研究将通过整体数据驱动的STIN架构、可通用的异常检测模型以及具有新型机器学习（ML）模型和框架的异常缓解方案进行。拟议的研究将通过创建可应用于任何stin相关网络的自主管理的方法和技术来推进该领域。这项研究将对学术界和工业界产生影响，并最终推动全球数字经济的发展。它还将有助于加拿大加强未服务和服务不足地区的数字基础设施的国家优先事项。HQP将接受培训，以开展关键研究目标，并获得独特的、可转移的机器学习、数据分析、网络和软件开发技能，使他们在学术界和工业界获得高度竞争力。

Reliable broadband Internet has never become so indispensable in our daily lives during and after the pandemic world. However, in 2021 alone, days of countrywide Internet outages have happened a few times. The Rogers network outage in April and social media service outage in October caused billion-dollar costs. The future Internet will face serious challenges. On the one hand, it is expected to provide global broadband access to more than 85% of the global population who currently have no broadband as reported by the International Telecommunications Union. On the other hand, highly resilient Internet access needs to be ensured with the increasing complexity of global telecommunications infrastructures in space and ground segments. With the new launch plans for massive non-geostationary (NGSO) satellites, the future Internet infrastructure will brace the convergence of satellite and terrestrial networks based on a new paradigm called satellite-terrestrial integrated network (STIN). A STIN is envisioned to enable global broadband Internet with the integration of advanced medium-Earth-orbit (MEO) and low-Earth-orbit (LEO) satellite constellations into heterogeneous ground segments. However, this vision can hardly be fulfilled without effective maintenance and operations for resilience assurance. For example, malfunctioning components, atmospheric events, or anomalous traffic on any network segment can easily degrade or disrupt network services. The complex, heterogeneous, and dynamic nature of the space and ground components introduces persistent operations challenges. Equipping STINs with an autonomous maintenance (AM) capability offers an ideal solution to the challenges. Such a solution can handle the increasing complexity of STINs and mitigate performance degradation. The long-term goal of the proposed research is to develop novel AM approaches to making the STIN-based Internet infrastructure resilient, intelligent, and self-maintainable. The short-term objectives are to explore the fundamental elements of AM in root cause analysis and anomaly mitigation for an NGSO-satellite-based STIN infrastructure. The proposed research will be conducted through holistic data-driven STIN architectures, generalizable anomaly detection models, and anomaly mitigation schemes with novel machine learning (ML) models and frameworks. The proposed research will advance the field by creating approaches and techniques that can be applied to autonomous management for any STIN-related network. The research will generate impact in academia and industry and ultimately boost the digital economy worldwide. It will also contribute to Canada's national priority for enhancing the digital infrastructure for unserved and underserved areas. HQP will be trained to conduct key research objectives and gain unique and transferrable skills sets of ML, data analytics, networking, and software development, making them highly competitive in securing positions in academia and industry.