Model of a Queuing System With BPP Elastic and Adaptive Traffic

Abstract

This paper presents an analytical model of a multiservice queueing system that services elastic and adaptive BPP traffic. The model presented in the paper was developed as an extension of earlier works published by the authors. The model is based on several concepts specific to modelling multi-service systems. In order to derive the model, the idea of equivalent bandwidth and the discretisation process were used first. Subsequently, the MIM-BPP method was generalised in relation to a system with traffic that undergoes the compression mechanism. Since the proposed model is an approximate one, the results of the calculation are compared with the simulation results. The comparison of the results confirms the accuracy of the proposed model.

High-Accuracy Analytical Model for Heterogeneous Cloud Systems with Limited Availability of Physical Machine Resources Based on Markov Chain

“…Erlang, which are described in the articles [27,42]. Moreover, the authors will attempt to develop a multi-parametric model under the accessibility limitations adopted in this article.…”

“…Another aspect important to consider in the future will be the use of cloud system resources and the application of cloud theory to optimize energy use [11,16,[43][44][45]. Thus, the authors plan to initiate research on multidimensional Markov chains considering the use of SD-FIFO input queue alongside Erlang, Engset, and Pascal type adaptive traffic [42]. The next important stage of research will be the application of overflows considering the MIM-NSD-BPP method [46] and applying the methodology for the multi-parametric model presented in [18,19].…”

An Analytical Model of a System with Compression and Queuing for Selected Traffic Flows

“…One such mechanism is compression [4][5][6][7], which makes it possible to introduce changes in the speed of generating data by its source in order to adjust or accommodate it to the current load of the network. In the case where overload is unavoidable, data can be placed in buffers (queuing mechanism) [8,9]. However, it is also important to take into consideration the fact that not all data streams can be additionally delayed because of a number of time constraints for a given service to which they are related.…”

“…Research into multi-service systems with compression and/or queuing is not the only ongoing research in contemporary research centres. It is worth highlighting studies that examine the properties of systems to which BPP traffic is offered [7,9,[11][12][13][14]. Analysing systems with BPP traffic allows for the inclusion of the actual number of traffic sources (e.g., network user devices) in analytical models.…”

Evaluation of the Accuracy of the Analytical Model of a Queuing System with a Finite-Compression Mechanism in Relation to Real Service Disciplines

“…For instance, the authors of [3][4][5][6][7][8] present models of a systems with dynamic resource reservation, restricting the utilisation of the system resources via selected calls. Conversely, the models proposed in [9][10][11][12][13][14][15][16][17][18] address the problem of traffic compression, which has become increasingly prevalent as transmission speeds fluctuate based on network load. Another notable mechanism is telecommunications traffic overflow, one of the earliest traffic management mechanisms, which was developed in the 1950s.…”