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Carmen Mas-Machuca

Publications and source records attributed to Carmen Mas-Machuca.

17 recordsLinked to original sources
arXiv2026

Fast-MCS: A Scalable Open-Source Tool to Find Minimal Cut Sets

A network is represented as a graph consisting of nodes and edges. A cut set for a source-destination pair in a network is a set of elements that, when failed, cause the source-destination pair to lose connectivity. A Minimal Cut Set (MCS) is a cut set that cannot be further reduced while maintaining its status as a cut set. MCSs are crucial in identifying the critical elements in the network that have the most significant impact on failure. This work introduces Fast-MCS, an open-source, scalable tool for evaluating MCSs in large, complex networks. Additionally, we compare the computation time of Fast-MCS with the state-of-the-art.

cs.NI↗
arXiv2025

How to build a sovereign network? -- A proposal to measure network sovereignty

Network sovereignty is a network operator's ability to reduce the dependency on component manufacturers to minimize the impact of manufacturer failures. Network operators now face new design challenges to increase network sovereignty and avoid vendor lock-in problems because a high dependency on a manufacturer corresponds to low survivability if that manufacturer is unavailable. The main contribution of this work is the proposal of a novel metric to measure network sovereignty, the Cut Set Coloring (CSC) score. Based on the CSC core metric CSC-ILP, our Integer Linear Program formulation is presented to maximize network sovereignty. We compare CSC-ILP's performance with state of the art manufacturer assignment strategies.

cs.NI↗
arXiv2024

Predicting Software Reliability in Softwarized Networks

Providing high quality software and evaluating the software reliability in softwarized networks are crucial for vendors and customers. These networks rely on open source code, which are sensitive to contain high number of bugs. Both, the knowledge about the code of previous releases as well as the bug history of the particular project can be used to evaluate the software reliability of a new software release based on SRGM. In this work a framework to predict the number of the bugs of a new release, as well as other reliability parameters, is proposed. An exemplary implementation of this framework to two particular open source projects, is described in detail. The difference between the prediction accuracy of the two projects is presented. Different alternatives to increase the prediction accuracy are proposed and compared in this paper.

cs.SE↗
arXiv2024

Bug Analysis Towards Bug Resolution Time Prediction

Bugs are inevitable in software development, and their reporting in open repositories can enhance software transparency and reliability assessment. This study aims to extract information from the issue tracking system Jira and proposes a methodology to estimate resolution time for new bugs. The methodology is applied to network project ONAP, addressing concerns of network operators and manufacturers. This research provides insights into bug resolution times and related aspects in network softwarization projects.

cs.SE↗
arXiv2024

Network Sovereignty: A Novel Metric and its Application on Network Design

Most network planning problems in literature consider metrics such as cost, availability, and other technology-aware attributes. However, network operators now face new challenges in designing their networks to minimize their dependencies on manufacturers. A low dependency is associated with higher network robustness in case one or more manufacturers fail due to erroneous component design, geopolitical banning of manufacturers, or other reasons discussed in this work. Our work discusses network sovereignty, i.e., the ability to operate a network without dependencies on a particular manufacturer while minimizing the impact of simultaneous manufacturer failure(s). Network sovereignty is considered by solving the manufacturer assignment problem in the network such that robustness is maximized. The three main contributions of this work are (i) the discussion of network sovereignty as a special attribute of dependability, (ii) the introduction of a novel metric -- the Path Set Diversity (PSD) score to measure network sovereignty, and (iii) the introduction of Naga, an ILP formulation to maximize network sovereignty using the PSD score. We compare Naga's performance with centrality metrics-based heuristics and an availability-based optimization. Our work aims to be the foundation to guide network operators in increasing their network sovereignty.

cs.NI↗
arXiv2024

Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection

In recent years, there has been a growing interest in using Machine Learning (ML), especially Deep Learning (DL) to solve Network Intrusion Detection (NID) problems. However, the feature distribution shift problem remains a difficulty, because the change in features' distributions over time negatively impacts the model's performance. As one promising solution, model pretraining has emerged as a novel training paradigm, which brings robustness against feature distribution shift and has proven to be successful in Computer Vision (CV) and Natural Language Processing (NLP). To verify whether this paradigm is beneficial for NID problem, we propose SwapCon, a ML model in the context of NID, which compresses shift-invariant feature information during the pretraining stage and refines during the finetuning stage. We exemplify the evidence of feature distribution shift using the Kyoto2006+ dataset. We demonstrate how pretraining a model with the proper size can increase robustness against feature distribution shifts by over 8%. Moreover, we show how an adequate numerical embedding strategy also enhances the performance of pretrained models. Further experiments show that the proposed SwapCon model also outperforms eXtreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) based models by a large margin.

cs.LG↗
arXiv2023

On the Benefits of Rate-Adaptive Transceivers: A Network Planning Study

Flexible-grid Elastic Optical Networks (EONs) have been widely deployed in recent years to support the growing demand for bandwidth-intensive applications. To address this cost-efficiently, optimized utilization of EONs is required. Next-generation bandwidth-variable transceivers (BVTs) will offer increased adaptivity in symbol rate as well as modulation through probabilistic constellation shaping. In this work, we therefore investigate the impact of increased configuration granularity on various aspects of optical networks. We account for practical implementation considerations of BVT configurations for the estimation of the required signal-to-noise ratio. Additionally, an optimization algorithm is presented that selects the most efficient configuration for each considered data rate and bandwidth combination. Based on the advanced transceiver configurations, we conduct a network planning study using a physical-layer-aware algorithm for flexible-grid EONs, and present results for a national and a continental optical backbone network topology. Our research demonstrates that a rise in modulation rate adaptivity results in substantial savings in resources, decreasing the number of necessary lightpaths by as much as 20% in EONs. In contrast, increased symbol rate granularity only results in minor savings.

cs.NI↗
arXiv2023

QoT estimation using EGN-assisted machine learning for multi-period network planning

The rapidly growing traffic demands in fiber-optical networks require flexibility and accuracy in configuring lightpaths, for which fast and accurate quality of transmission (QoT) estimation is of pivotal importance. This paper introduces a machine learning (ML)-based QoT estimation approach that meets these requirements. The proposed gradient-boosting ML model uses precomputed per-channel self-channel-interference values as representative and condensed features to estimate non-linear interference in a flexible-grid network. With an enhanced Gaussian noise (GN) model simulation as the baseline, the ML model achieves a mean absolute signal-to-noise ratio error of approximately 0.1 dB, which is an improvement over the GN model. For three different network topologies and network planning approaches of varying complexities, a multi-period network planning study is performed in which ML and GN are compared as path computation elements (PCEs). The results show that the ML PCE is capable of matching or slightly improving the performance of the GN PCE on all topologies while reducing significantly the computation time of network planning by up to 70%.

cs.NI↗
arXiv2023

Multi-Wavelength Transponders for High-capacity Optical Networks: A Physical-layer-aware Network Planning Study

Continued cost- and power-efficient capacity scaling in optical networks is imperative to keep pace with ever-increasing traffic demands. In this paper, we investigate multi-wavelength transponders as a potential way forward. Suitable system architectures and realistic specifications of multi-wavelength transponders are identified and analyzed in terms of transmit OSNR penalties and spectral constraints. We investigate the performance for different specifications as compared to single-wavelength transponders in a network planning study on two network topologies, developing guidelines for multi-wavelength transponders specifications and their potential benefits. The studies show a reduction in the number of required lasers of up to 83% at the expense of a slight increase in number of lightpaths, demonstrating the potential for significant cost savings and efficiency improvements.

cs.NI↗
arXiv2023

Optical Networks and Interconnects

The rapid evolution of communication technologies such as 5G and beyond, rely on optical networks to support the challenging and ambitious requirements that include both capacity and reliability. This chapter begins by giving an overview of the evolution of optical access networks, focusing on Passive Optical Networks (PONs). The development of the different PON standards and requirements aiming at longer reach, higher client count and delivered bandwidth are presented. PON virtualization is also introduced as the flexibility enabler. Triggered by the increase of bandwidth supported by access and aggregation network segments, core networks have also evolved, as presented in the second part of the chapter. Scaling the physical infrastructure requires high investment and hence, operators are considering alternatives to optimize the use of the existing capacity. This chapter introduces different planning problems such as Routing and Spectrum Assignment problems, placement problems for regenerators and wavelength converters, and how to offer resilience to different failures. An overview of control and management is also provided. Moreover, motivated by the increasing importance of data storage and data processing, this chapter also addresses different aspects of optical data center interconnects. Data centers have become critical infrastructure to operate any service. They are also forced to take advantage of optical technology in order to keep up with the growing capacity demand and power consumption. This chapter gives an overview of different optical data center network architectures as well as some expected directions to improve the resource utilization and increase the network capacity.

cs.NI↗
arXiv2022

Transmission-Aware Bandwidth Variable Transceiver Allocation in DWDM Optical Networks

This paper addresses the transmission-aware transceiver allocation problem of flexible optical networks for a multi-period planning. The proposed approach aims at assigning the best configuration of bandwidth variable transceivers (BVTRX) considering the amplifier noise and nonlinear channel interferences using the incoherent Extended Gaussian Noise (EGN) model. The proposed solution improves the network throughput and spectrum utilization in the early planning periods and allocates lower number of BV-TRXs in later periods in comparison to algorithms presented recently. A heuristic approach to regenerator placement has also been applied achieving up to 25% transceiver and 50% spectrum utilization savings in comparison to configurations without regenerators.

cs.NI↗
arXiv2018

Approaches to Enhancing Cyber Resilience: Report of the North Atlantic Treaty Organization (NATO) Workshop IST-153

This report summarizes the discussions and findings of the 2017 North Atlantic Treaty Organization (NATO) Workshop, IST-153, on Cyber Resilience, held in Munich, Germany, on 23-25 October 2017, at the University of Bundeswehr. Despite continual progress in managing risks in the cyber domain, anticipation and prevention of all possible attacks and malfunctions are not feasible for the current or future systems comprising the cyber infrastructure. Therefore, interest in cyber resilience (as opposed to merely risk-based approaches) is increasing rapidly, in literature and in practice. Unlike concepts of risk or robustness - which are often and incorrectly conflated with resilience - resiliency refers to the system's ability to recover or regenerate its performance to a sufficient level after an unexpected impact produces a degradation of its performance. The exact relation among resilience, risk, and robustness has not been well articulated technically. The presentations and discussions at the workshop yielded this report. It focuses on the following topics that the participants of the workshop saw as particularly important: fundamental properties of cyber resilience; approaches to measuring and modeling cyber resilience; mission modeling for cyber resilience; systems engineering for cyber resilience, and dynamic defense as a path toward cyber resilience.

cs.CR↗