  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.
