基于随机网络演算的TTE网络时延分析
收稿日期: 2015-07-08
修回日期: 2015-11-13
网络出版日期: 2015-11-23
基金资助
国家自然科学基金(61301086);中央高校基本科研业务费专项资金(YWF-14-DZXY-018,YWF-14-DZXY-023)
Stochastic network calculus for analysis of latency on TTEthernet network
Received date: 2015-07-08
Revised date: 2015-11-13
Online published: 2015-11-23
Supported by
National Natural Science Foundation of China (61301086);the Fundamental Research Funds for the Central Universities (YWF-14-DZXY-018, YWF-14-DZXY-023)
时间触发以太网(TTE)是一种新颖的混合型时间触发和事件触发的通信网络,通过引入时间触发(TT)流量,增强了航空电子全双工(AFDX)交换式以太网的确定性。虽然TT流量具有完全的时间确定性,但是与AFDX中虚拟链路(VL)兼容的速率约束(RC)流量仍具有一定非确定性。传统用于AFDX网络实时性能分析的方法在考虑TT流量固定分区调度时隙的影响下已不再适用,为了保障RC流量的实时性能,分别提出了基于确定性网络演算和随机网络演算两种延迟分析模型。在确定性网络演算下,通过构造TT流量的聚合到达曲线和RC流量的服务曲线以得到RC的确定性延迟上界;在随机网络演算下,通过切诺夫(Chernoff)边界定理构造RC流量的两状态伯努利分布模型,得到概率保证下的延迟上界。对比实验结果表明:随机网络演算模型可以有效减小确定性网络演算模型对RC流量性能分析的悲观性,同时从一定程度上验证了两种理论分析模型的正确性。
赵露茜 , 李峭 , 林晚晴 , 熊华钢 . 基于随机网络演算的TTE网络时延分析[J]. 航空学报, 2016 , 37(6) : 1953 -1961 . DOI: 10.7527/S1000-6893.2015.0305
Time-triggered Ethernet (TTE) is a novel hybrid communication network with time-triggered and event-triggered messages. The determinacy of avionics full duplex (AFDX) switched Ethernet is improved by introducing the time-trigger (TT) flow. Even though TT flow has full time certainty, rate-constraint (RC) flow which is compatible with the virtual link (VL) in the AFDX network is still uncertain. Since the influence of fixed schedule slots of TT flow, traditional performance analysis methods used in the AFDX network are not suitable for the. In order to guarantee the real-time performance of RC flow, we propose two latency analysis models respectively based on the deterministic network calculus and stochastic network calculus. Under the deterministic network calculus, the latency upper bounds of RC flow are obtained by constructing the aggregate arrival curve of TT flow and service curve for RC flow. Under the stochastic network calculus, we compute the probabilistic upper bounds for RC flow by constructing two states Bernoulli distribution models for RC flow through Chernoff bound. Comparison results show that stochastic network calculus reduces the pessimism of deterministic network calculus on the performance analysis of RC flow. In addition, simulation results verify the correctness of two theoretical methods in some degree.
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