The rapid availability of new services means that network operators cannot exhaustively test their impact on the network or anticipate any capacity exhaustion. This situation will be worse with the imminent introduction of 5G technology and the kind of totally new services that it will support. In addition, the increasing complexity of the network makes analyzing its behavior challenging against the specific traffic that needs to be supported; this prevents from training human operators and, much less, machine learning algorithms that might automatize network operation. In this paper, we present CURSA-SQ, a methodology to analyze network behavior when specific traffic that would be generated by groups of service consumers is injected. CURSA-SQ includes input traffic flow modeling with second and sub-second granularity based on specific service and consumer behavior, as well as a continuous G/G/1/k queue model based on the logistic function. The methodology allows for accurately studying the traffic flows at the input and outputs of complex scenarios with multiples queue systems, as well as other metrics such as delays, while showing noticeable scalability. Application use cases include packet and optical network planning, service introduction assessment, and autonomic networking, just to mention a few.
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