Research on Flow Scheduling of Wired and Wireless Converged Satellite Time Sensitive Network
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摘要: 随着空间通信任务日趋复杂化，尤其是对时间敏感的需求不断提升，一方面要求星内系统的高带宽、可靠性和实时性；另一方面星间无线链路也应具备低时延和高可靠性。但由于卫星内部有线链路与星间无线链路差异大，业务数据经过有线和无线链路联合传输时，容易引发节点拥塞，而无法保障时敏业务的时延有界需求。为了提升数据在空间网络传输的实时性，该文提出了一种有线无线融合的时间敏感网络(TSN)流调度方案，首先对有线和无线链路资源分配与终端时延关系进行分析建模，并通过TSN控制器收集终端时敏需求，构建以全网时敏业务端到端最小平均时延为优化目标，然后采用基于增强精英保留遗传算法进行方案的快速求解。通过Pycharm对比测试时隙分配算法的性能，同时设计实现基于EXata网络仿真平台的低轨卫星TSN系统，并搭建实验场景进行试验验证。测试结果表明，该文所提出的流联合调度方案能够为空间时敏任务提供有界、稳定的时延保障。Abstract: With the increasing complexity of space communication tasks, especially the increasing demand for time sensitivity, on the one hand, the performances of high bandwidth, high reliability and real time in intra satellite systems are required; on the other hand, low latency and high reliability are demanded for inter satellite wireless links. However, due to the large difference between the satellite internal wired link and the inter-satellite wireless link, when the data is transmitted from the wired link to wireless link, it is easy to cause congestion problem on node, and can not guarantee the bounded latency for time-sensitive service. To improve the real time performance for data transmission in space information network, a flow scheduling scheme in wired and wireless converged Time-Sensitive Network (TSN) is proposed. Firstly, the relationship between terminal delay requirement and wired/wireless link resources is analyzed. Then, the time-sensitive requirements of terminals are collected by TSN controller and the optimization goal is determined by the end-to-end minimum average delay of time-sensitive flow in the entire network. Moreover, the enhanced elite retention genetic algorithm is adopted to solve quickly the scheme. Finally, the performance of proposed time slot allocation algorithm is verified through Pycharm. Meanwhile, a low-orbit satellite network scenario is implemented under EXata network simulation platform to evaluate the proposed scheme in the further. The results demonstrate that the proposed wired and wireless joint stream scheduling scheme can provide bounded and stable delay for space time-sensitive tasks.
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表 1 TSN-GA算法
输入：网络设备数N，每个设备权值$ {\alpha _i} $，无线传输速率v，设备
业务量大小$ B_i^{\text{s}} $，种群数量Np，最大迭代数maxCycle，决策变量
维度Dim，变异概率$ {P_m} $，交叉概率$ {P_c} $输出：网络最低时延、时隙最佳位置 (1) while $ {\text{Cycle}} \le {\text{maxCycle}} $ do (2) for m=1 to Np (3) $ v_m^n $=Crossover($ \alpha _m^n $) (4) end for (5) for m=1 to Np (6) $ \mu _m^n $=Mutation($ v_m^n $) (7) end for (8) for m=1 to 2Np (9) $ \rho _m^n $=Select($ \alpha _m^n $, $ \mu _m^n $) (10) end for (11) $ {\text{Cycle}} $=$ {\text{Cycle}} $+1; (12) end while (13) for Cycle=1 to maxCycle (14) $ {\text{min\_delay(Cycle) = F(Cycle)}} $； (15) $ {\text{best\_position}} $(Cycle)= $ {\text{F(Cycle)}} $ (16) end for 
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表 2 编队卫星的参数设置
参数 数值 TSN控制卫星轨道参数[a,e,i,$\varOmega$,w,f] 8×106 m, 0°, 30°, 15°, 15°, 10° 空间圆半径(R/km) 30 编队卫星个数 7 成员卫星1初始相位角$ {\theta _1} $(°) 0 相位之差$ \Delta \theta $(°) 72
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表 3 实验参数设置
业务类型 源端 目的端 周期(ms) 业务量(bit)
快速位置报告信息终端11 终端33 6 500 终端18 终端33 6 500 终端14 终端33 6 500
北斗短报文终端13 终端37 12 2000 终端8 终端15 12 2000 终端23 终端29 12 2000
遥感勘测终端12 终端15 24 4000 终端18 终端17 24 4000 终端29 终端12 24 4000
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