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Steady-state solutions for a variety of relevant queueing systems are knowntoday, e.g., from queueing theory, effective bandwidths, and network calculus.The behavior during transient phases, on the other hand, is understood to amuch lesser extent as its analysis poses significant challenges. Consideringthe majority of short-lived flows, transient effects that have diverse causes,such as TCP slow start, sleep scheduling in wireless networks, or signalling incellular networks, are, however, predominant. This paper contributes a generalmodel of regenerative service processes to characterize the transient behaviorof systems. The model leads to a notion of non-stationary service curves thatcan be conveniently integrated into the framework of the stochastic networkcalculus. We derive respective models of sleep scheduling and show thesignificant impact of transient phases on backlogs and delays. We also considermeasurement methods that estimate the service of an unknown system fromobservations of selected probe traffic. We find that the prevailing ratescanning method does not recover the service during transient phases well. Thislimitation is fundamental as it is explained by the non-convexity ofnon-stationary service curves. A second key difficulty is proven to be due tothe super-additivity of network service processes. We devise a novel two-phaseprobing technique that first determines a minimal pattern of probe traffic.This probe is used to obtain an accurate estimate of the unknown transientservice.

A Non-stationary Service Curve Model for Performance Analysis of Transient Phases