The adequate modeling of input processes often requires that correlation is taken into account and is a key issue in building realistic simulation models. In analytical modeling Markovian Arrival Processes (MAPs) are commonly used to describe correlated arrivals, whereas for simulation often ARMA/ARTA-based models are in use. Determining the parameters for the latter input models is well-known whereas good fitting methods for MAPs have been developed only in recent years. Since MAPs may as well be used in simulation models, it is natural to compare them with ARMA/ARTA models according to their expressiveness and modeling capabilities for dependent sequences. In this paper we experimentally compare MAPs and ARMA/ARTA-based models.
Large Service Oriented Architectures (SOAs) have to fulfill qualitative and quantitative requirements. Usually Service Level Agreements (SLAs) are defined to fix the maximal load the system can accept and the minimal performance and dependability requirements the system has to provide. In a complex SOA where services use other services and thus performance and dependability of a service depend on the performance and dependability of lower level services, it is hard to give reasonable bounds for quantitative measures without performing experiments with the whole system. Since field experiments are too costly, model based analysis, often using simulation is a reliable alternative. The paper presents an approach to model complex SOAs and the corresponding SLAs hierarchically, map the model on a simulator and analyze the model to validate or disprove the different SLAs.
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