用生成式AI智能采样并压缩网络监控数据，节省超50%传输成本。
GO-GenZip: Goal-Oriented Generative Sampling and Hybrid Compression
- 根据下游任务需求，动态选择要采集的数据
- 实测数据传输成本降低50%以上，分析精度基本不变
- 适合需要实时处理海量网络数据的运维团队
当前网络数据遥测系统依赖来自多个分布式源的大量细粒度关键性能指标（KPI），导致存储、传输和实时分析日益不可持续。本文提出一种目标导向的生成式AI（GenAI）驱动采样与混合压缩框架，重新设计网络遥测流程。不同于传统被动压缩全量数据的方法，本方法联合优化采集内容与编码方式，以信息对下游任务的相关性为指导。框架融合自适应采样策略（采用自适应掩码技术）与生成建模，识别时空维度上的关键模式并保留核心特征。选定数据再通过混合压缩方案处理，结合传统无损编码与生成式AI驱动的有损压缩。在真实网络数据集上的实验表明，采样与数据传输成本减少超过50%，同时保持与原数据相当的重建精度及目标导向分析保真度。
原文摘要 · Abstract (English)
Current network data telemetry pipelines consist of massive streams of fine-grained Key Performance Indicators (KPIs) from multiple distributed sources towards central aggregators, making data storage, transmission, and real-time analysis increasingly unsustainable. This work presents a generative AI (GenAI)-driven sampling and hybrid compression framework that redesigns network telemetry from a goal-oriented perspective. Unlike conventional approaches that passively compress fully observed data, our approach jointly optimizes what to observe and how to encode it, guided by the relevance of information to downstream tasks. The framework integrates adaptive sampling policies, using adaptive masking techniques, with generative modeling to identify patterns and preserve critical features across temporal and spatial dimensions. The selectively acquired data are further processed through a hybrid compression scheme that combines traditional lossless coding with GenAI-driven, lossy compression. Experimental results on real network datasets demonstrate over 50$\%$ reductions in sampling and data transfer costs, while maintaining comparable reconstruction accuracy and goal-oriented analytical fidelity in downstream tasks.
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