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面向低轨卫星网络的星上分布式Sketch测量框架OA

On-board distributed Sketch measurement framework for LEO satellite networks

中文摘要英文摘要

针对低轨卫星网络星地链路频繁切换导致流量跨星分散、难以追踪与汇聚的问题,提出一种星上分布式Sketch测量框架.该框架利用分布式哈希表动态记录跨星长流经过的卫星轨迹,在查询时先定位轨迹序列,再聚合各接入卫星上的流量片段.采用卫星数据平面与地面控制平面协同架构:卫星端部署Sketch进行本地统计,并利用布隆过滤器与分布式哈希表实现首包检测与轨迹记录;地面端负责按需查询、结果聚合与参数配置,借助Sketch的线性可加性高效汇聚分散片段.理论分析与仿真实验表明,在792颗卫星Walker星座、跨3颗测量卫星和1 200条跨星长流的典型场景下,所提测量方案的平均端到端查询时延为128 ms,单星存储开销低于100 KB.

Frequent handovers in low earth orbit(LEO)satellite networks fragment traffic and complicate the tracking and aggregation of long flows.To address this issue,a distributed Sketch-based measurement framework was proposed.The framework employed a distributed hash table to record the satellite trajectory of each long flow.During a query,it first located the trajectory sequence and then aggregated the traffic fragments collected from the access satellites.A coop-erative architecture consisting of a satellite data plane and a ground control plane was designed.The satellite side de-ployed Sketch for local traffic counting and used a Bloom filter together with a distributed hash table to detect the first packet and record trajectory information.The ground side supported on-demand queries,result aggregation,and param-eter configuration,and it exploited the linearity of Sketch to efficiently merge distributed traffic fragments.Experimental results demonstrate that,in a typical scenario of a 792-satellite Walker constellation with 3 measurement satellites tra-versed and 1 200 cross-satellite long flows,the proposed scheme achieves an average end-to-end query latency of 128 ms and a per-satellite storage overhead of less than 100 KB.

张妍;潘恬;郑䶮;刘江;黄韬

紫金山实验室,江苏 南京 211111紫金山实验室,江苏 南京 211111||北京邮电大学信息与通信工程学院,北京 100876紫金山实验室,江苏 南京 211111紫金山实验室,江苏 南京 211111||北京邮电大学信息与通信工程学院,北京 100876紫金山实验室,江苏 南京 211111||北京邮电大学信息与通信工程学院,北京 100876

信息技术与安全科学

低轨卫星网络流量测量Sketch分布式哈希表布隆过滤器

LEO satellite networktraffic measurementSketchdistributed hash tableBloom filter

《通信学报》 2026 (9)

133-147,15

国家自然科学基金资助项目(No.62372053) The National Natural Science Foundation of China(No.62372053)

10.11959/j.issn.1000-436x.TXXB260286

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