A popular branch of stochastic network calculus (SNC) utilizes moment-generating functions (MGFs) to characterize arrivals and services, which enables end-to-end performance analysis. However, existing traffic models for SNC cannot effectively represent the complicated nature of real-world network traffic such as dramatic burstiness. To conquer this challenge, we propose an adaptive spatial-temporal traffic model: dMAPAR-HMM. Specifically, we model the temporal on-off switching process as a dual Markovian arrival process (dMAP) and the arrivals during the on phases as an autoregressive hidden Markov model (AR-HMM). The dMAPAR-HMM model fits in with the MGF-SNC analysis framework, unifies various state-of-the-art arrival models, and matches real-world data more closely. We perform extensive experiments with real-world traces under different network topologies and utilization levels. Experimental results show that dMAPAR-HMM significantly outperforms prevailing models in MGF-SNC.

翻译：随机网络演算（SNC）的一个主流分支利用矩母函数（MGF）刻画到达流与服务过程，从而可实现端到端性能分析。然而，现有SNC流量模型难以有效表征真实网络流量的复杂特性（如突发性）。为解决这一挑战，我们提出一种自适应时空流量模型：dMAPAR-HMM。具体而言，我们将时域开关切换过程建模为双马尔可夫到达过程（dMAP），并将开启阶段的到达过程建模为自回归隐马尔可夫模型（AR-HMM）。该dMAPAR-HMM模型兼容MGF-SNC分析框架，统一了多种前沿到达模型，且更贴合真实数据。我们在不同网络拓扑与负载水平下使用真实流量轨迹开展大量实验。结果表明，dMAPAR-HMM在MGF-SNC中显著优于主流模型。