Max-Min Fairness in WMNs with Interference Cancelation Using Overheard Transmissions

We show an impact of using interference cancelation mechanisms for signals that have been overheard in the past on performance of fair wireless mesh networks. In our research we show that even in those very restricted conditions and max-min cost function, the idea of interference cancelation can significantly increase the capacity of such networks. In order to approximate possible advantages of using interference cancelation in the considered conditions, we propose a novel MIP model that allows for calculating perfect scheduling and maximal throughput in a network. We compare the results with cases when the interference cancelation mechanisms are disabled. Our results show that using interference cancelation mechanisms for signals that have been overheard in the past increases a network throughput by 40% on average in approximately 20% of test cases

Resilient arcs and node disjointness in diverse routing

In multi-layer networks protection can be provided at multiple layers. Hence some links at an upper layer may be {\it resilient} because they are protected at a lower layer. We will designate as {\it resilient arc} at a given layer, an arc which has some form of protection at an underlaying layer. When path diversity is used at an upper layer, and resilient arcs are taken into account, it may not be necessary for the considered paths to be fully disjoint. We solve a problem of finding the shortest node-disjoint pair of paths that can share resilient arcs. It is assumed that a network consists of a set of nodes and a set of arcs. Moreover, a number of available arcs are resilient. Our goal is to find the shortest pair of paths such that they share only those nodes that are incident to shared resilient arcs. Moreover, we assume that the resulting paths cannot contain loops, and costs of shared resilient arcs are counted only once towards the objective function. In the paper we present two novel algorithms solving the above problem, and two supporting algorithms that are utilized as subroutines. We implement the proposed algorithms and compare them to an MIP approach

Resilient arcs and node disjointness in diverse routing

In multi-layer networks protection can be provided at multiple layers. Hence some links at an upper layer may be {\it resilient} because they are protected at a lower layer. We will designate as {\it resilient arc} at a given layer, an arc which has some form of protection at an underlaying layer. When path diversity is used at an upper layer, and resilient arcs are taken into account, it may not be necessary for the considered paths to be fully disjoint. We solve a problem of finding the shortest node-disjoint pair of paths that can share resilient arcs. It is assumed that a network consists of a set of nodes and a set of arcs. Moreover, a number of available arcs are resilient. Our goal is to find the shortest pair of paths such that they share only those nodes that are incident to shared resilient arcs. Moreover, we assume that the resulting paths cannot contain loops, and costs of shared resilient arcs are counted only once towards the objective function. In the paper we present two novel algorithms solving the above problem, and two supporting algorithms that are utilized as subroutines. We implement the proposed algorithms and compare them to an MIP approach

Ultra-Wideband WDM Optical Network Optimization

Ultra-wideband wavelength division multiplexed networks enable operators to use more effectively the bandwidth offered by a single fiber pair and thus make significant savings, both in operational and capital expenditures. The main objective of this study is to minimize optical node resources, such as transponders, multiplexers and wavelength selective switches, needed to provide and maintain high quality of network services, in ultra-wideband wavelength division multiplexed networks, at low cost. A model based on integer programming is proposed, which includes a detailed description of optical network nodal resources. The developed optimization tools are used to study the ultra-wideband wavelength division multiplexed network performance when compared with the traditional C-band wavelength division multiplexed networks. The analysis is carried out for realistic networks of different dimensions and traffic demand sets

Optimization of Optical Networks Based on CDC-ROADM Technology

New generation of optical nodes in dense wavelength division multiplexed networks enables operators to improve service flexibility and make significant savings, both in operational and capital expenditures. Thus the main objective of the study is to minimize optical node resources, such as transponders, multiplexers and wavelength selective switches, needed to provide and maintain high quality dense wavelength division multiplexed network services using new generation of optical nodes. A model based on integer programming is proposed, which includes a detailed description of an optical network node. The impact on the network performance of conventional reconfigurable optical add drop multiplexer technology is compared with colorless, directionless and contentionless approaches. The main focus of the presented study is the analysis of the network congestion problem arising in the context of both reconfigurable optical add drop multiplexer technologies. The analysis is supported by results of numerical experiments carried out for realistic networks of different dimensions and traffic demand sets

Artificial Intelligence Control Logic in Next-Generation Programmable Networks

The new generation of programmable networks allow mechanisms to be deployed for the efficient control of dynamic bandwidth allocation and ensure Quality of Service (QoS) in terms of Key Performance Indicators (KPIs) for delay or loss sensitive Internet of Things (IoT) services. To achieve flexible, dynamic and automated network resource management in Software-Defined Networking (SDN), Artificial Intelligence (AI) algorithms can provide an effective solution. In the paper, we propose the solution for network resources allocation, where the AI algorithm is responsible for controlling intent-based routing in SDN. The paper focuses on the problem of optimal switching of intents between two designated paths using the Deep-Q-Learning approach based on an artificial neural network. The proposed algorithm is the main novelty of this paper. The Developed Networked Application Emulation System (NAPES) allows the AI solution to be tested with different patterns to evaluate the performance of the proposed solution. The AI algorithm was trained to maximize the total throughput in the network and effective network utilization. The results presented confirm the validity of applied AI approach to the problem of improving network performance in next-generation networks and the usefulness of the NAPES traffic generator for efficient economical and technical deployment in IoT networking systems evaluation