The extension of elastic optical network (EON) technologies to multi-band transmission (MB-EON) promises enhanced spectral efficiency, greater throughput, and long-term cost benefits for telecom operators. However, designing such networks presents challenges, particularly in optimizing physical parameters like optical power and quality of transmission (QoT) across different frequency bands. This paper introduces a methodology for optimal span-by-span power allocation using two hyper-accelerated power optimization (HPO) modes: flat launch power (FLP) and flat received power (FRP). This methodology significantly accelerate network power optimization while ensuring service stability in scenarios such as changes in network parameters, QoT degradation due to aging, and network re-optimization or upgrading. Through a comprehensive comparison, we find that FRP notably improves signal flatness and GSNR/OSNR, particularly in the S-band, contributing to a network-wide throughput increase in the order of 12% to 75%. Additionally, we demonstrate that HPO applied to global power optimization is simpler and more cost-effective than when applied to local methods for large-scale networks.

The performance of conversational Large Language Models (LLMs) in general, and of ChatGPT in particular, is currently being evaluated on many different tasks, from logical reasoning or math to answering questions on a myriad of topics. Instead, much less attention is being devoted to the study of the linguistic features of the texts generated by these LLMs. This is surprising since LLMs are models for language, and understanding how they use the language is important. Indeed, conversational LLMs are poised to have a significant impact on the evolution of languages as they may eventually dominate the creation of new text. This means that for example, if conversational LLMs do not use a word it may become less and less frequent and eventually stop being used altogether. Therefore, evaluating the linguistic features of the text they produce and how those depend on the model parameters is the first step toward understanding the potential impact of conversational LLMs on the evolution of languages. In this article, we consider the evaluation of the lexical diversity of the text generated by LLMs in English and how it depends on the model parameters. A methodology is presented and used to conduct a comprehensive evaluation of lexical diversity using ChatGPT as a case study. The results show how lexical diversity depends on the version of ChatGPT and some of its parameters, such as the presence penalty, or the role assigned to the model. The dataset and tools used in our analysis are released under open licenses with the goal of drawing much-needed attention to the evaluation of the linguistic features of LLM-generated text.

In this paper, we have generated and examined three different datasets based on the symbol rate and traffic load of the link states to estimate the quality of transmission (QoT) in terms of Generalized Signal to the Noise Ratio (GSNR) for an optical span. The results reveal that in datasets generated for different symbol rates and traffic loads, the outlier data degrades the accuracy in estimating the GSNR. The results show that nearly 99% of the mismatchings had less than 0.3 dB of error for a synthesised dataset in which the symbol rate and traffic load considered fixed.

This paper proposes a shared precomputed restoration (SPR) mechanism for link failures in translucent elastic optical networks (EONs). We present SPR with a heuristic algorithm that aims to minimize a cost function. The cost function depends on the number of transceivers and frequency slots (FSs) used to establish a working/protection lightpath (LP) with a tunable parameter that determines the weight of the number of transceivers and FSs in the cost function. SPR precalculates a protection LP for all links of each working LP. As a result, non-link-disjoint working LPs can share protection spectrum and transceivers on newly eased conditions. Like shared backup path protection (SBPP) and dedicated protection (1+1), SPR guarantees single link failure recovery. Our simulation results reveal that SPR outperforms SBPP in terms of recovered bandwidth in multiple link failures. Furthermore, SPR uses fewer transceivers compared to SBPP and 1+1 protection.

Learned Bloom Filters (LBFs) have been recently proposed as an alternative to traditional Bloom filters that can reduce the amount of memory needed to achieve a target false positive probability when representing a given set of elements. LBFs rely on Machine Learning models combined with traditional Bloom filters. However, if LBFs are going to be used as an alternative to Bloom filters, their security must be also be considered. In this paper, the security of LBFs is studied for the first time and a vulnerability different from those of traditional Bloom filters is uncovered. In more detail, an attacker can easily create a set of elements that are not in the filter with a much larger false positive probability than the target for which the filter has been designed. The constructed attack set can then be used to for example launch a denial of service attack against the system that uses the LBF. A malicious URL case study is used to illustrate the proposed attacks and show their effectiveness in increasing the false positive probability of LBFs. The dataset under consideration includes nearly 485K URLs where 16.47% of them are malicious URLs. Unfortunately, it seems that mitigating this vulnerability is not straightforward.

Building Metropolitan Area Networks (MAN) for supporting 5G services and Multi-access Edge Computing (MEC) requires carefully rethinking of MAN node architectures, featuring both computing and storage capabilities. This article provides an example of such MEC-enabled node architecture following the Central-Office Re-architected as Datacenter (CORD) guidelines and further explores both optical, packet-switching and computing/storage hardware needs for the expected traffic requirements for MANs in the short, medium and long term. We show that replacing traditional routers with carrier-class possibly disaggregated switches offers appreciable saving opportunities on both cost and power in the packet layer. On the other hand, introducing datacenter capabilities at MAN nodes may increase the network total cost by a value between 30-50%, plus another important amount in power consumption. Thus, new design of MAN nodes for incorporating such computing/storage capabilities must be carefully decided on which scenarios it makes sense and profit, given the large CAPEX and OPEX impact.

Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time.

Android malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and metadata to identify patterns in malware applications. Our experiments show the following: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android markets are more relevant in detecting malware, such as the application developer and certificate issuer; and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.

This work performs a deep analysis on the behaviour of Anti-Virus (AV) engines regarding Android malware detection. A large dataset, with more than 80K apk files tagged as Malware by one or many AV engines is used in the analysis. With the help of association rule learning, we show interesting patterns and dependencies between different AV engines.

Optical Code Division Multiple Access (OCDMA) techniques have shown outstanding capabilities in the sharing of optical media, in particular in access networks. However, OCDMA systems may suffer from Multiple Access Interference (MAI) and other kinds of noise when many users access the shared media simultaneously, increasing the BER (Bit Error Rate) to unacceptable levels, that is, a situation at which all combined signals interfere and are lost. This work proposes a mixed OCDMA and Tunable Transmitter-Fixed Receiver (TT-FR) WDM and ring architecture at which the ring is split into small-size segments to limit the probability of MAI. Essentially, every segment in the ring has got two hub nodes (on the segment's head and tail) which forwards inter-segment traffic to other hub nodes on dedicated home wavelengths, thus making use of WDM. The access media inside the segment is shared between the nodes by means of OCDMA, and code reuse is possible on different segments. Our performance analysis shows how to split a given ring into segments in order to minimise the BER due to multiple users accessing the network and allow for high bit-rates for a given traffic load. In addition, we analyse the possibility of introducing Forward Error Correction (FEC) at a moderate overhead cost to improve performance.

Building vehicular networks in roads and highways is a challenging research topic with a large number of applications ranging from traffic jams and car collisions prevention to efficient route planning. The analysis of the distance between vehicles in roads is a key factor in, e.g., designing vehicular networks protocols or planning a supporting infrastructure to improve vehicular connectivity. This work proposes a Gaussian-exponential mixture model to characterize the time distance between vehicles in a highway lane, based on measurements collected at different locations in several highways of the city of Madrid, in Spain. The model arises from the observed behavior that some vehicles travel very close together, like in a burst mode, showing Gaussian inter-arrival times, while other vehicles are somehow isolated, showing exponentially distributed inter-arrival times. The experiments show that such a Gaussian-exponential mixture model accurately characterizes inter-vehicle times observed from real traces.

The increasing amount of bandwidth requirements and quality of service needs for the next-generation access networks has boosted extensive research in the fiber-optics communication field. In this light, passive optical networks (PONs) combined with optical code division multiple access (OCDMA), provide a potentially cost-effective solution to meet such bandwidth demands. This work proposes an optical transparent architecture which enables all-optical communication between the network nodes. The encoded data streams are multiplexed at a merging point which results in multiple user interference (MUI), thus significantly reducing the network throughput. The networking nodes are able to monitor and record user activity in the PON, and further register the (past) state of activity at the merging point. In this work, we study the coherence of state between the networking nodes and the merging point, for different packet size distributions, in order to predict an optimal transmission instant of each node's data packets. We note that the states are coherent depending on the packet size distribution.

In the development of accurate capacity planning and network resource dimensioning models, network operators must handle representative information about the traffic volumes traversing its network. However, the amount of traffic measurements available over which to perform such analysis, processing and storage is overwhelming. For this reason, the research community has understood the importance of finding an effective mechanism to reduce (or subsample) such huge amount of data, with minimum loss of information.

The optical burst switching (OBS) paradigm proposes a new set of transmission protocols and network architectures that permits the high-utilization of the raw bandwidth available by dense wavelength division multiplexing at a moderate computational cost. Accordingly, it is necessary to extend the management plane of such networks to include the particular aspects of OBS, in order to guarantee the appropriate network operation. This paper analyzes the information required for the management of such OBS networks and proposes a new information model based on observed management use-cases. A simple data model is also specified to reduce the computational burden, but compliant with the modeled information.

This work provides a jitter analysis of size-based burst assembly algorithms and also discusses other burst assembly algorithms that use the packet delay as the assembly threshold to provide a bound on jitter.