Modeling of Multi-Service Switching Networks With Multicast Connections

Abstract

This article presents an analytical model of a switching network that can be used in the nodes of a large number of modern multi-service networks. The model of a multi-service threestage switching network proposed in this article has the advantage of providing the possibility to evaluate the point-to-point blocking probability for multicast connections. The information obtained in this way can then be used both at the stage of the estimation of the capacity of a new network and during the optimisation process of an existing network. In the calculations performed during the investigations, three scenarios for the fan out of multicast connections in the switching network are taken into consideration. The assumption is that the branching of a connection can occur in the first, second and the third stage of the switching network. The model proposed in the article is based on the concept of "effective" availability. The article discusses the methods for the determination of the effective availability parameter for successive connections that belong to a given multicast connection. The results of the analytical modelling are compared with the simulation data for three selected structures of multi-service switching networks to which different mixtures of multi-service BPP traffic are offered. The results of the calculations and the data provided by the simulation experiments validate and confirm high accuracy of the proposed model. This allows the model to be easily applied in practice to different environments to evaluate the capacities of the nodes in multi-service networks.

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

“…The analytical models presented in Sections 3.1 and 3.2 are well known in the literature of the subject and have been successfully used for years for the analysis, design and optimization of ICT systems. These models are used to analyze switching fields [28], physical cloud computing infrastructure [19] or prioritized systems [29]. Regardless of the current work on the systems under consideration, the books [30,31] provide a coherent summary of knowledge on these models and their applications.…”

Multicast Space-Conversion-Space Strict-Sense Nonblocking Switching Fabrics with Multicast Opportunity in the Last Stage for Continuous Multislot Connections

“…Network nodes, such as routers, must be able to route information from one source to multiple destinations. The fundamental component of a network node is a switching fabric that must support this type of communication [2][3][4][5].…”

“…Strict-sense nonblocking switching fabrics typically require more equipment than blocking fabrics and other types of nonblocking fabrics, but they have simpler control (path-searching algorithms) [2,[10][11][12][13][14][15][16][17]. Understanding nonblocking structures is important because they represent the upper limit of the equipment's requirements.…”

“…Furthermore, nonblocking structures often serve as a starting point for the study of blocking structures. We can remove equipment from a known switching fabric architecture and analyze the probability of loss of connection in such a modified structure [2,[18][19][20]. Therefore, studying the nonblocking conditions of various switching fabric structures is of significant importance in switching theory [10][11][12][13][14][15][16][17][21][22][23][24][25].…”

The New Analytical Model of Free Resources in the Limited Availability Resources

“…Managing the allocation of resources in network systems and mitigating the risks associated with fluctuations in network traffic are critical challenges for operators operating in dynamic and demanding environments [2]- [3]. Load balancing, the art of distributing network traffic between multiple resources, plays a key role in ensuring optimal utilization of network systems, maintaining adequate service responsiveness and delivering a service with guaranteed quality parameters to the end user [4]- [6]. Similarly, the identification of potential risks and the implementation of appropriate mitigation measures are important for the smooth delivery of services and minimizing the impact of unpredictable and sudden spikes in resource access requests [7]- [11].…”