A GRASP-based network re-optimization strategy for improving RWA in multi-constrained optical transport infrastructures

Enhancement of wireless sensor network lifetime by deploying heterogeneous nodes

Network performance metrics such as available bandwidth and latency are essential to achieve good Quality of Service (QoS) in multimedia streaming. There are unique requirements in network performance metrics for media applications, such as audio conferencing, video streaming, video conferencing, and high-definition (HD) video conferencing. In this paper, we focus on conference call type suggestion based on link quality prediction. The link's quality is classified based on the available bandwidth and latency between two network nodes. We have implemented and compared two of the most popular supervised learning based classification methods, i.e. logistic regression and support vector machine (SVM). We have compared the performance of both methods and their suitability to apply in link quality prediction. The experimental results show that SVM outperforms logistic regression for binary and multiclass classification in terms of accuracy.

This paper quantifies the impact of topological characteristics on the performance of single radio multichannel IEEE 802.11 mesh networks. Topological characteristics are the number of nodes per subnetwork, the hop count, the neighbor node density, the hidden nodes, the number of nodes in the neighborhood of the gateway, and the hidden nodes in the neighborhood of the gateway. Network performance metrics are throughput, fairness and delay. The data mining Support Vector Machine (SVM) model was used to extract the relationships between the network topology metrics and the network performance metrics based on data results obtained through ns-2 simulation of random networks. The results obtained can be used as a basis to design channel assignment algorithms or to aid the deployment and management of single radio wireless mesh networks.

In 5G networks, time-series data will be omnipresent for the monitoring and management of network performance metrics. With the increase in the number of Internet of Things (IoT) devices, it is expected that the number of real-time time-series data streams will increase at a fast pace, making forecasting essential for the proactive successful management of the network. In this article, we discuss to use both linear and non-linear forecasting methods, including machine learning, deep learning, and neural networks to improve 5G networks’ management. For this purpose, we design and implement a real-time distributed forecasting framework, used to make simultaneous predictions of different network performance metrics, and with different learning algorithms. By using our framework, we compare the use of forecasting methods in two network scenarios, in a real vehicular network and in a 4G network, representing two different slices in a 5G network. We also integrate our framework in a 5G architecture. Using the best forecasting models assessed previously, we propose a dynamic threshold algorithm for multi-slice management, to ensure that the resources of each slice are updated according to the slices’ needs, while avoiding congestion and saving resources for other slices. The experimental results show that it is possible to forecast the slices’ needs and congestion probability, selecting the best forecasting approach or an ensemble of the best ones, and act accordingly in the network to optimize its management.

Several active queue management (AQM) schemes have been proposed to address the bufferbloat issue, a long network delay and performance degradation issue caused by excess buffering of packets in network nodes. The solutions proposed up to now, however, satisfy only a subset of the key network performance metrics, i.e., end-to-end throughput, throughput fairness, and delay, in 802.11s-based multi-radio multi-channel wireless mesh networks (MRMC WMN). In this letter, we propose FD-AQM, a fairness-aware delay-controlled AQM to satisfy all three key network performance metrics simultaneously in the 802.11s-based MRMC WMN. The proposed FD-AQM achieves the goal by adopting a per-queue drop interval which is recalibrated according to the average round-trip time of flows, and a drop decision mechanism which considers average throughput as well as target delay. Through simulations, we show that FD-AQM is superior to other AQM schemes in terms of throughput and fairness.

With the rapid development of Internet of Things (IoT) technology, the number of nodes in wireless sensor networks (WSNs) is explosively increasing, and the scale of network is increased gradually. Traditional single-layer non-clustering network is no longer suitable for current WSNs, which results in high maintenance cost and fast deterioration of network performance. By analyzing the impact of existing static and dynamic clustering schemes on network performance, it is concluded that additional factors need to be considered to improve the overall performance of the network, such as residual energy of nodes, number of neighbor nodes and load balancing. Therefore, an adaptive multi-layer clustering networking strategy based on capability weights is proposed. Based on the real-time changes of each cluster density, node load and residual energy, the node capacity weights are updated dynamically according to the actual network performance, then the cluster heads are renewed adaptively. By comparing the performance metrics in the experiments, proposed strategy can effectively reduce the load of key nodes such as cluster head, and improves the network performance metrics such as average transmission delay, average transmission hops and load balancing.

We categorized VPN’s impact in to two different aspects; The affecting aspect, and the affected aspect. The affecting aspect of the impact encompasses factors such as security, algorithms, hardware, and software. Whereas the affected aspect is of the likes of network performance. While VPNs have managed to integrate security, one of the affecting aspect of the impact on one hand, on the other hand, VPNs should be regarded as a potential threat to network performance. In this study, for affordability purpose, we choose to use NS-2 simulated test-bed to shed light on the VPN’s performance impact in a network. Considering the most common network performance metrics, throughput and delay; we assessed these performance metrics by means of average and percentage changes theories. The findings emphasize quantitative impact on the TCP/IP throughput than on its counterpart UDP/IP. We finally developed an analytical equation to model this VPN’s performance impact.

Network design and allocation considerations in the Hughes data-flow machine

Το αντικείμενο της διατριβής αυτής είναι η μελέτη της διαστρωματικής (cross-layer) προσέγγισης ανάπτυξης ασύρματων δικτύων κατανεμημένης λειτουργίας με απαιτήσεις επικοινωνίας πραγματικού χρόνου και περιορισμένους διαθέσιμους πόρους. Επιπλέον βασικό στόχο αποτελεί και η σχεδίαση, πρόταση αντίστοιχης αρχιτεκτονικής η οποία στοχεύει στη βέλτιστη διαχείριση διαθεσίμων δικτυακών πόρων σε καταστάσεις συμφόρησης του δικτύου και κατά συνέπεια στην βελτίωση της απόδοσης αυτού. Μέσα από μελέτες στα πλαίσια της διατριβής αναδεικνύεται η σημασία του φαινομένου συμφόρησης ως κύριου παράγοντα σπατάλης δικτυακών πόρων καθώς και οδήγησης του δικτύου σε κατάσταση κορεσμού με αρνητική επίπτωση σε όλες τις παραμέτρους απόδοσης του δικτύου. Στόχος, λοιπόν, της προτεινόμενης επικοινωνιακής αρχιτεκτονικής είναι η αποφυγή του φαινομένου συμφόρησης έτσι ώστε το δίκτυο να οδηγείται δυναμικά σε ένα σταθερό σημείο απόδοσης (όσο αυτό είναι δυνατό) το οποίο θα επιτρέπει στο δίκτυο να αποδίδει όσο το δυνατόν καλύτερα αποφεύγοντας τη ίδια στιγμή άσκοπη σπατάλη πόρων. Βασική παράμετρος στη σχεδίαση αποτελεί η συμβατότητα τόσο ως προς σημαντικό εύρος διαθέσιμων πρωτοκόλλων σε διάφορα επίπεδα, όσο και ως προς τη δυνατότητα συνύπαρξης κόμβων που ενσωματώνουν την προτεινόμενη αρχιτεκτονική με κόμβους χωρίς αυτήν. Η προτεινόμενη αρχιτεκτονική υλοποιήθηκε στα πλαίσια γνωστού και αξιόπιστου δικτυακού εξομοίωση. Αξιολόγηση της υλοποίησης αυτής μέσα από μεγάλο αριθμό πειραμάτων έδειξε επίτευξη του στόχου καθώς το φαινόμενο της συμφόρησης αντιμετωπίστηκε σε όλες τις περιπτώσεις με σημαντικά οφέλη στην απόδοση του δικτύου και διαχείριση των πόρων. Επιπλέον, λόγω του μεγάλου βαθμού παραμετροποίησης αποτελεί ιδανική βάση για μελλοντικές προεκτάσεις.

The exponential growth in mobile data traffic, coupled with finite radio frequency resources, has intensified the challenges associated with efficient radio access network (RAN) planning and optimization in spectrum-constrained environments. As operators strive to meet increasing demands for high-speed connectivity and low-latency services, there is an urgent need for innovative strategies that can maximize spectral efficiency while ensuring quality of service (QoS) and user experience. This paper proposes a conceptual model for radio access planning and optimization specifically tailored to spectrum-limited settings, with a focus on dynamic resource allocation, interference management, and adaptive network configurations. The proposed model integrates multi-layered radio planning techniques, including spectrum reuse, frequency refarming, and heterogeneous network deployment, supported by real-time data analytics and artificial intelligence. The model introduces a feedback-driven architecture that continuously monitors network performance metrics—such as signal-to-interference-plus-noise ratio (SINR), throughput, and user mobility patterns—to guide adaptive reconfiguration of RAN parameters. Furthermore, it incorporates a spectrum-aware decision engine that enables efficient traffic offloading and load balancing across macro, micro, and small cells. To ensure robustness, the model accounts for regulatory constraints, spectrum licensing scenarios (licensed, unlicensed, and shared bands), and co-existence with legacy technologies. Emphasis is placed on the strategic placement of base stations, beamforming techniques, and interference coordination schemes such as enhanced Inter-Cell Interference Coordination (eICIC) and Coordinated Multipoint (CoMP). The framework also facilitates spectrum aggregation, dynamic carrier selection, and energy-efficient operations to enhance capacity without additional spectrum acquisition. This conceptual model serves as a valuable tool for mobile network operators, planners, and policymakers aiming to optimize network coverage and capacity under spectrum scarcity. It provides a scalable and technology-neutral blueprint applicable to 4G LTE, 5G NR, and emerging radio access technologies. Future work will focus on validating the model through simulations and real-world testbeds to refine its components and quantify its performance gains in diverse deployment scenarios.

Consumers of IP network performance metrics have many different uses in mind. This memo provides long-term reporting considerations (e.g., hours, days, weeks, or months, as opposed to 10 seconds), based on analysis of the points of view of two key audiences. It describes how these audience categories affect the selection of metric parameters and options when seeking information that serves their needs. This document is not an Internet Standards Track specification; it is published for informational purposes.

The exponential growth of distributed artificial intelligence training workloads has created unprecedented challenges in network performance management and optimization. Traditional monitoring approaches fail to adequately address the dynamic and complex communication patterns inherent in distributed AI systems. This paper presents a comprehensive machine learning-based framework for network performance monitoring and prediction specifically designed for distributed AI training environments. Our approach leverages advanced feature engineering techniques to capture multi-dimensional network performance metrics, including latency variations, bandwidth utilization patterns, and communication bottlenecks. We develop and evaluate multiple machine learning models, including gradient boosting machines, neural networks, and ensemble methods, to predict network performance degradation before it impacts training efficiency. Experimental evaluation on real-world distributed training scenarios demonstrates that our framework achieves 94.7% prediction accuracy for network latency spikes and reduces training time by 23.4% through proactive performance optimization. The proposed monitoring architecture provides real-time insights into network behavior patterns, enabling adaptive resource allocation and communication scheduling optimization. Our contributions include a novel feature extraction methodology, a comprehensive performance prediction model, and a scalable monitoring framework that can be deployed across heterogeneous distributed computing environments.

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. In this paper, the focus is to enhance the intelligence of the microgrid network using a multi-agent system and validation is using network performance metrics such as delay, throughput and jitter. Network performance is analyzed for the medium-scale microgrid using two IEEE test systems, i.e., IEEE 34 and IEEE 39. In this paper, Bellman–Ford algorithm is incorporated to calculate the shortest path to a given destination. The algorithm is defined for the distributed nature of the microgrid. From this model, researchers have achieved up to 30% improvement in the network performance of a microgrid.

In wireless sensor networks (WSNs), preserving energy requires utmost attention, as they are highly resource constrained. Clustering is commonly considered as one of the efficient energy conservation techniques. In a clustered WSN, due to the nature of operation, cluster heads (CHs) near the sink bear the major share of data forwarding compared with the CHs far away from the sink, resulting in an energy hole problem. First, we have analyzed the optimization of network lifetime by balancing the energy consumption among different CHs. To meet the requirement of optimization of network lifetime, we have devised a routing-aware optimal clustering strategy. Further, keeping the network model in mind, we identify Archimedes' spiral, based on which a deployment function is proposed for distributing a member node and a CH. The performance of the optimal clustering strategy is evaluated in terms of energy balance and network lifetime. Simulation results show that our scheme provides satisfactory network performance in terms of end-to-end delay and throughput. Finally, all the results are compared with two competing schemes that confirm our scheme's supremacy in terms of both design performance metrics as well as network performance metrics.

Network delays and delay variations are two of the most important network performance metrics directly impacting real-time applications such as voice over IP and time-critical financial transactions. This importance is illustrated by past work on understanding the delay constancy of Internet paths and recent work on predicting network delays using virtual coordinate systems. Merely understanding currently observed delays is insufficient, as network performance can degrade not only due to traffic variability but also as a result of routing changes. Unfortunately this latter effect so far has been ignored in understanding and predicting delay related performance metrics of Internet paths. Our work is the first to address this short coming by systematically analyzing changes in network delays and jitter of a diverse and comprehensive set of Internet paths. Using empirical measurements, we illustrate that routing changes can result in roundtrip delay increase of converged paths by more than 1 second. Surprisingly, intradomain routing changes can also cause such large delay increase. Given these observations, we develop a framework to analyze in detail the impact of routing changes on network delays between end-hosts. Using topology information and properties associated with routing changes, we explain the causes for observed delay fluctuations and more importantly identify routing changes that lead to predictable effects on delay-related metrics. Using our framework, we study the predictability of delay and jitter changes in response to both passively observed interdomain and actively measured intradomain routing changes.