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Abstract--Resource sharing between Book-Ahead (BA) and Instantaneous Request (IR) reservation often results in high preemption rate of on-going IR calls. High IR call preemption rate causes interruption to service continuity which is considered as detrimental in a QoS-enabled network. A number of call admission control models have been proposed in literature to reduce the preemption rate of on-going IR calls. Many of these models use a tuning parameter to achieve certain level of preemption rate. This paper presents an Artificial Neural Network (ANN) model to dynamically control the preemption rate of on-going calls in a QoS-enabled network. The model maps network traffic parameters and desired level of preemption rate into appropriate tuning parameter. Once trained, this model can be used to automatically estimate the tuning parameter value necessary to achieve the desired level of preemption rate. Simulation results show that the preemption rate attained by the model closely matches with the target rate.

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Performance models of server systems, based on layered queues, may be very complex. This is particularly true for cloud-based systems based on microservices, which may have hundreds of distinct components, and for models derived by automated data analysis. Often only a few of these many components determine the system performance, and a smaller simplified model is all that is needed. To assist an analyst, this work describes a focused model which includes the important components (the focus ) and aggregates the rest in groups, called dependency groups. The method Focus-based Simplification with Preservation of Tasks (FSPT) described here fills an important gap in a previous method by the same authors. The use of focused models for sensitivity predictions is evaluated empirically in the paper on a large set of randomly generated models. It is found that the accuracy depends on a “saturation ratio” SR between the highest utilization value in the model and the highest value of a component excluded from the focus; evidence suggests that SR must be at least 2, and must be larger to evaluate larger model changes. This dependency was captured in an “Accurate Sensitivity Hypothesis” based on SR, which can be used to indicate trustable sensitivity results.