Spiridoula V. Margariti

Ensuring Quality of Service (QoS) in complex, large scale, and centralized Software-Defined Networking (SDN) environments remains a major challenge for the scientific community, network administrators or operators, and industry. The SDN networking model promises to improve and optimize QoS in dynamic environments by enhancing network applications related to traffic management. It provides centralized control, flexibility, abstraction, and programmability of network resources. However, these expectations have not been fully met, primarily due to the difficulties associated with integrating or adapting traditional QoS mechanisms (routing, queue management, resource reservation) into the SDN architecture, the limitations of the OpenFlow protocol, and the highly dynamic nature of modern networks. Given the wide variety of networking environments, such as Internet of Things (IoT), data centers, cloud compounding, fog computing, 5G/6G networks and others, the core challenge is the lack of a standardized workflow for the integration of QoS mechanisms into SDN. Previous studies primarily focus on specific QoS mechanisms in SDN, particular application domains, or architectural aspects, offering limited comprehensive comparisons of QoS requirements, implementation approaches, evaluation methodologies, and the limitations of individual studies. To address these gaps, this study combines bibliometric analysis with a systematic review of selected publications from 2020 to 2026, providing an up-to-date and comprehensive overview of the latest research advances, emerging trends, and technological developments in the field. Furthermore, it critically examines the QoS requirements, implementation approaches, evaluation methodologies, and limitations reported in the selected studies, thereby offering a structured synthesis of the current state of the art and identifying promising directions for future research.

Distributed computing environments, such as fog computing, are attracting increasing attention because they can bring data processing and storage closer to data sources and adapt to the ever-increasing demands of existing and emerging applications executed at the network edge. Workflow scheduling poses a significant challenge in these systems, hindering the full exploitation of their potential. However, workflow scheduling is an NP-hard problem. Despite the numerous existing approaches for addressing this problem, several limitations remain, including high execution latency, suboptimal scheduling performance, and inefficient utilization of the available computational resources. To address these challenges, this work proposes a novel hybrid metaheuristic mechanism, called EHHO-GWO, for workflow scheduling, with the objective of optimizing execution time, monetary cost, and energy consumption. Moreover, the proposed approach is comparatively evaluated against classical optimization algorithms, namely Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), as well as other recent metaheuristic approaches. Using FogWorkflowSim as the simulation framework and a set of scientific workflows as input, this study evaluates the effectiveness and efficiency of the proposed algorithm and demonstrates its superior performance in fog computing environments. The experimental results are statistically validated using the Wilcoxon signed-rank test, revealing consistent performance improvements over well-known competing scheduling algorithms.

5G technology was introduced in 2019 with the aim of transforming digital connectivity, enabling a new generation of communication capabilities, such as significantly faster mobile broadband, highly reliable low-latency links, and the capacity to support vast IoT deployments. However, the expected improvements promised by 5G technology do not seem to be reflected in actual usage. This study aims to address the issue of the real-world usage of 5G telecommunications networks and compare it with the theoretical specifications of the network as officially published by 3GPP. Specifically, the focus will be on the evaluation of the implementation of the 5G network in northwestern Greece, which operates in Non-Standalone (NSA) mode as of the date of this study’s completion. 5G Standalone (SA) networks were not available for public testing in this region during the data collection period. The analysis focuses on key performance indicators, including throughput, latency, stability, and coverage, to assess how effectively current deployments meet the expectations set by 5G standards. Results show that while 5G delivers notable improvements in peak data rates and latency, several practical limitations persist. NSA deployments remain constrained by their dependence on 4G infrastructure, resource sharing between LTE and 5G components affects performance under high-load conditions, and inconsistent coverage leads to significant variability in user experience. These findings highlight the gap between theoretical capabilities and operational performance, offering insights that can guide future network optimization and inform the transition toward 5G Standalone (SA) architectures.

The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, network monitoring, traffic engineering, dynamic resource allocation planning, and applying Quality of Service (QoS) policies. The programmability nature of SDN, the holistic view of the network through SDN controllers, and the capability for dynamic adjustable and reconfigurable controllersare fertile ground for the development of new techniques for traffic classification. Although there are enough research works that have studied traffic classification methods in SDN environments, they have several shortcomings and gaps that need to be further investigated. In this study, we investigated traffic classification methods in SDN using publicly available SDN traffic trace datasets. We apply a series of classifiers, such as MLP (BFGS), FC2 (RBF), FC2 (MLP), Decision Tree, SVM, and GENCLASS, and evaluate their performance in terms of accuracy, detection rate, and precision. Of the methods used, GenClass appears to be more accurate in separating the categories of the problem than the rest, and this is reflected in both precision and recall. The key element of the GenClass method is that it can generate classification rules programmatically and detect the hidden associations that exist between the problem features and the desired classes. However, Genetic Programming-based techniques require significantly higher execution time compared to other machine learning techniques. This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features.

Software-defined network (SDN) technology can offer wireless networks the advantages of simplified control and network management. This SDN subdomain technology is called the software-defined wireless network (SDWN). In this study, the performance of four controllers in an SDWN environment is assessed, since the controller is the most significant component of the entire network. Using the Mininet-WiFi platform, the performance of each controller is evaluated in terms of throughput, latency, jitter, and packet loss. Moreover, a multi-criteria evaluation is introduced and applied to provide a fair comparison between SDWNs. This study provides an appropriate configuration of SDWNs that is useful for network engineering and can be used for SDWNs performance optimization.

This research paper investigates the importance of traceability in the meat industry. It explains that the global market’s intense competition and consumers’ increased expectations have forced companies to implement electronic traceability systems to improve efficiency, reduce errors, and mitigate incidents and fraud. The meat industry is facing increased consumer awareness and concern about food quality and safety. Consumers are extremely sensitive to food production conditions, and traceability can help companies increase consumers’ trust. This work describes the design and implementation of a customized traceability system, developed using the Agile method, for the local meat industry. For the needs of the project, advanced and innovative information and communication technologies and tools, such as cloud computing and the Internet of Things, were also used. The main problem that this research aims to address is the lack of transparency in three phases: transparency within the industry’s internal processes, transparency in the life of the animal from the moment of its birth, and transparency towards the consumer. The objectives of this research are twofold. First, we aim to digitize the Greek meat industry. Concurrently, we intend to create a traceability system that will generate important data, thereby providing valuable information for all stakeholders.

Purpose: The objective of this study is to improve the food supply chain performance taking into consideration the fundamental concepts of traceability by combining the current frameworks, its principles, its implications and the emerging technologies.Design/methodology/approach: A narrative literature review of already existing empirical research on traceability systems was conducted resulting in 862 relevant papers. Following a step-by-step sampling process, the authors ended up with 46 final samples for the literature review.Findings: The main findings of this study include the various descriptions of the architecture of traceability systems, the different sources enabling this practice, the common desirable attributes, and the enabling technologies for the deployment and implementation of traceability systems. Moreover, several technological solutions are presented, which are currently available for traceability systems, and finally, opportunities for future research are provided.Practical implications: It provides an insight, which could affect the implementation process of traceability in the food supply chain and consequently the effective management of a food traceability system (FTS). Managers will be able to create a traceability system, which meets users' requirements, thus enhancing the value of products and food companies.Originality/value: This study contributes to the food supply chain and the traceability systems literature by creating a holistic picture of where something has been and where it should go. It is a starting point for each food company to design and manage its traceability system more effectively.

Fog computing is an emerging and evolving technology, which bridges the cloud with the network edges, allowing computing to work in a decentralized manner. As such, it introduces a number of complex issues to the research community and the industry alike. Both of them have to deal with many open challenges including architecture standardization, resource management and placement, service management, Quality of Service (QoS), communication, participation, to name a few. In this work, we provide a comprehensive literature review along two axes—modeling with an emphasis in the proposed fog computing architectures and simulation which investigates the simulation tools which can be used to develop and evaluate novel fog-related ideas.