Advanced Tensor Approach to Fast Reroute With Quality of Service Protection Under Multiple Parameters

Abstract

Background. The paper proposes a solution to such an urgent problem today, as to ensure the fault tolerance of infocommunication networks with the support of the required level of quality of service. The proposed solution is based on the implementation of an advanced tensor approach to fast reroute with the protection of the level of quality of service under multiple parameters. Objective. The aim of the article is to improve the flow-based fast rerouting model in the infocommunication network, which is based on updated conditions for ensuring quality of service in terms of bandwidth, average end-to-end delay, and probability of packet loss. It was possible to obtain updated conditions for ensuring the quality of service through the use of a tensor approach to modeling infocommunication networks. Methods. As research methods, graph theory, tensor theory, queuing systems were used. For mathematical modeling and experimental studies, the MatLab simulation package was used. Results. As a result of the study, under the conditions of implementing fast rerouting, it was possible to provide the required level of quality of service in the infocommunication network. At the same time, with an increase in QoS requirements, thanks to an improved tensor approach, the updated conditions for ensuring the quality of service while implementing fast rerouting were adequate, which, as a result, contributed to a more efficient use of the available network resource. Conclusions. Implementation of an improved tensor approach to solving the problem of fast rerouting will ensure the fault tolerance of the infocommunication network with the protection of the level of quality of service in terms of bandwidth, average end-to-end delay, and the probability of packet loss.

Research and Development of Delay-Sensitive Routing Tensor Model in IoT Core Networks

“…However, when it comes to meeting the requirements for quantitative values of fundamental QoS indicators in solving routing problems, there is a need to introduce into the model or method of routing in one or another form the mathematical expressions to calculate these QoS indicators. Given that the formulas for calculating QoS indicators are exclusively nonlinear functions of network parameters and traffic characteristics, the optimization routing problems themselves acquire a nonlinear form, which requires the use of appropriate methods for their solution, such as nonlinear programming [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Furthermore, if it is necessary to consider the network dynamics in the formalization and solution of optimization routing problems, most network parameters also become a function of time [ 38 ].…”

“…Furthermore, the flow conservation conditions considering the probable loss of packets on each of the network routers take the following form [ 17 , 23 ]: where is the source router (sender) of packets; is the destination router (receiver) of packets of the th flow; and is the fraction of the th flow of packets that have been successfully transmitted in the network from the source router to the destination.…”

“…Conditions to prevent overloading of links (8) is one of the variants of requirements for providing QoS in terms of network bandwidth. In addition, according to models (1)–(8) [ 23 ]: …”

“…The mathematical expression for the calculation can be obtained in the process of tensor generalization of the model (1)–(8) [ 23 ]. The network links are additionally represented by a set of edges , where is the total number of links in the network.…”

“…Therefore, this article aims to study the tensor model of delay-sensitive routing in communication networks proposed in [ 23 ], which is presented in the coordinate system of interpolar paths and internal node pairs. The proposed tensor model contains new mathematical expressions for calculating end-to-end Quality of Service indicators, namely average end-to-end delay and packet loss probability, which are valid in conditions of implementation dynamic and multipath routing strategies.…”