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Machine Learning-Based Network Analytics, Troubleshoot, and Self-Healing Holistic Telemetry System Incorporating Modem-Embedded Machine Analysis of Multi-Protocol Stacks
Machine Learning-Based Network Analytics, Troubleshoot, and Self-Healing Holistic Telemetry System Incorporating Modem-Embedded Machine Analysis of Multi-Protocol Stacks
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机译:基于机器学习的网络分析、故障排除和自愈整体遥测系统,包括多协议栈的调制解调器嵌入式机器分析
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摘要
A novel machine learning-based network analytics, troubleshoot, and self-healing holistic telemetry system is configured to perform modem-embedded machine analysis of multi-protocol stacks (e.g. OSI model stacks) simultaneously from one integrated coherent diagnostic system alone, and identify sources of data network problems autonomously within an entire end-to-end network topology of a network operator, while not necessitating human diagnosis of the data network problems. This system uniquely embeds a smart universal telemetry (SUT) as a quality-of-experience (QoE) parameter collection agent in intermediary transport-level network equipment and each end-user modem, which in turn enables periodic or on-demand collection of robust diagnostic data from all end-user modems and intermediary transport level nodes in a data network. By executing a machine learning (ML)-based artificial intelligence (AI) analytical module in a cloud-computing resource, the system then achieves autonomous identification and source pinpointing of network problems, and in some cases, self-repairs machine-identified data network problems autonomously.
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