MIMO simulator of call server input lines occupancy

Abstract

This paper presents and solves the problems of modeling and designing the necessary capacity and the adequate capacity of accepting input connections serving the calls into the system. The main aim of the research work is finding the optimum number of input server connections while minimizing the number of rejected requests according to a specific maximum number of expected calls in a specific time-interval, i.e. at peak-hour. With the results obtained we wish to model and optimize the planning and the dimensioning of the processing server as well as reduce the costs of this, since hiring an input line actually presents quite a substantial cost. Therefore it is necessary to first determine how many input connections are needed to serve a certain quota of users at a specific moment by using the methods of statistical modeling. On the basis of obtained results we can then assess whether a certain segment has too many or not enough input connections. The objective of the presented multiple-input and multiple-output (MIMO) simulator is to raise the level and the quality of service and at the same time lower the costs of hiring input connections. This paper presents the key segments composing the call and server system (ordinary, lamer and dummy caller model, statistical Gaussian curve of calls distribution, mechanisms of accepting and rejecting calls, management of input connections capacity, random call triggering, etc.). The above-mentioned segments represent the models and the sub-models of the simulator. They have been derived using the methods of statistical modeling. The optimum solution can be found manually or automatically using the method of automation of simulation runs and incrementing/decrementing the parameter of the number of input connections into the system. Searching the optimum number of input connections manually is an entirely empirical method, where the user manually changes the mentioned parameter, and is looking for a scenario in which the result of the simulator regarding the number of rejected calls is minimal. With an automatic search the simulator automatically generates the number of runs with incrementing and decrementing the mentioned parameter in each, and thus automatically finds the optimum solution. This paper also presents an automatic analysis of simulation runs and a statistical final report, which includes a conclusion on the results obtained in different scenarios.

Method for Planning, Optimizing, and Regulating EV Charging Infrastructure

“…Still, their occurrence and the formation of patterns are entirely stochastic. Predicting the "rush hour" periods is also a part of the deterministic segment, as we were able to precisely assess when these periods occur for the Margento service system [44] (analysis of statistical data). For this reason, the proposed method has only the interval of maximum service system load determined deterministically.…”

“…For optimization, we first used the method of incrementation/decrementation of the service capacity parameter in terms of the allowed threshold or criterion of rejected users. Because the used method has certain deficiencies (very time-consuming-see paper [44] about looking for a solution), we provided two different processes to speed up the search for an optimal solution. These are:…”

“…For the threshold of 10% (Figure 5), we quickly obtain a not necessarily optimal solution. One can find the optimal solution by further bisection of the interval, but the incrementation method mentioned here serves this purpose better [44]. From this perspective, we find an approximation with a secant method.…”

“…During the simulation, statistical data are collected by accepting, terminating, and rejecting users. Therefore, statistical information is continuously updated on the simulation/emulation tool [44].…”

Modeling and design of a Session Initiation Protocol overload control algorithm

“…32 Modeling and simulation can help enterprise engineers to optimize the planning and the dimensioning of their communication network or system. 33 Numerous models for different communication systems have been developed recently, for example, a model was proposed by Fras et al 32 to mimic the packet defragmentation process based on the measured network traffic. In order to find the optimum number of input connections required for serving a certain quota of users, Klampfer et al 33 presented a model for predicting the system capacity of accepting the calls into a server.…”