Cloud computing plays a crucial role in the Industry 4.0 era, particularly with the rise of Internet of Things (IoT) applications that support domains such as education, healthcare, business, and manufacturing. These applications consist of multiple services with diverse quality of service (QoS) requirements, making their development and deployment complex. While traditional cloud environments provide scalability, they often fail to support latency-sensitive and resource-intensive applications. To overcome these limitations, alternative paradigms such as Cloud–Fog–Edge (CFE), Cloud–Fog (CF), Cloud–Edge (CE), Fog–Edge (FE), and Mobile Edge Computing (MEC) have emerged. These models push computation, storage, and networking closer to end devices, reducing latency and bandwidth usage. However, the heterogeneity, mobility, and dynamic nature of these environments make service placement (a known NP-hard problem) a central challenge. This article presents a systematic literature review of service placement approaches across the compute continuum. Following established SLR methodology, we identified and analyzed 124 peer-reviewed studies published between 2018 and 2024, classifying them by (i) deployment environment, (ii) service placement strategies and algorithms, (iii) adaptability of the solution, (iv) optimization objectives, (v) virtualization/orchestration technologies, (vi) evaluation methodologies, including workloads, testbeds, and simulation tools and (vii) use cases or application types. The novelty of this work lies in providing not only a detailed taxonomy of placement approaches but also this is the first survey that takes all seven aspects into consideration and establishes correlations between them. Our findings reveal that most existing works target smart health applications and favor heuristic-based placement in complex CFE scenarios, while research on scientific and compute-intensive workloads remains limited. We also identify Kubernetes as the most widely used orchestration technology and latency as the dominant optimization metric. Despite significant progress, the field is still maturing, with gaps in real-world validation and adaptive, ML-based placement strategies. By consolidating technical approaches, evaluation practices, and open challenges, this survey offers both researchers and practitioners a structured overview of the state of the art and guidance for advancing service placement in the compute continuum.

A Digital Twin (DT) is a virtual representation of both cyber and physical objects. Initially, the concept was introduced to optimize manufacturing products, aimed at validating and verifying their behavior along their lifecycle. However, in the recent years and with the advent of Industry 4.0, it has also gained huge attention in the cybersecurity arena. Indeed, using a DT is seen as an strategy to automate, monitor, simulate, and diagnose cyber–physical systems behavior, aimed at making them to be secure. The DT can provide security in multiple domains such as manufacturing, aerospace, automotive, construction, smart grid, smart cities, and smart transportation. The fundamentals of a DT is a combination of data and a model of the system. To this end, this survey aims to explore current efforts on DT modeling techniques, and how these DT models capture the behavior of the system to enhance cybersecurity. The study focuses on the construction of behavioral models, techniques, tools, and technologies of DT used for specific security applications. The purpose of the discussion carried out in this survey is to illustrate the reader on how these behavioral models adapt to different security applications. In addition, the paper also analyzes and classifies current DT literature based on use cases, type of DT study, tools, and various DT operation modes with their specific security application. The findings of this survey describe security applications being enhanced by DT, and how the combination of key technologies facilitating Industry 4.0 (i.e., AI, IoT, Big Data and Cloud Computing, among others), are accelerating the adoption of DT as a solution for next generation security applications. Finally, this survey highlights relevant research gaps that need to be addressed before the wide adoption of DT in cybersecurity. • Provides a review of the DT modeling techniques applied to cyber security. • An in-depth analysis of recent work related to DT in enhancing security applications. • Discussion is provided on security applications, tools and architecture through DT literature analysis. • Future directions for security enhancing DT are also described.

This paper presents a novel data architecture designed specifically for smart advertising applications, focusing on the efficient flow of data while ensuring high security and data integrity. The research leverages federated learning as a black box approach, emphasizing data authenticity, privacy, and the early removal of unwanted or fake data. The proposed data model adopts a semi-random role assignment method based on various criteria to collect and aggregate data. The architecture consists of model nodes, data nodes, and validator nodes, assigned to each node based on factors such as computing power, connection quality, and historical performance. By selecting only a fraction of nodes for modeling and validation tasks, the proposed architecture optimizes resource consumption and minimizes data loss. The AROUND social network has been used as a case study to demonstrate the effectiveness of the proposed data architecture. Both, the simulation and practical implementations demonstrate that it can reduce network traffic and average CPU usage by more than 50% when the number of users increased 20 times, while maintaining the performance of the recommender model.

Smart advertising is growing in popularity and affecting businesses. Smart advertising is a more friendly, inter-active, personalised, and creative method of promoting a product, and attempts to delight clients. AROUND is a social networking service that emphasizes smart advertising through an effective recommender system. The system considers user profiles, history, social network connections, mood, and IoT-supported positioning to select the most relevant ads using machine learning technology. Although the current deployment of the AROUND system is based on the cloud, an edge-based architecture provides relevant improvement in terms of system response time. In this paper we extend the edge-based strategy to leverage the potential of federated learning on multiple distributed edge servers. We show that federated learning can take advantage of the distributed nature of the system, and leverage the specificities of local features. In fact, in this research, we propose a novel federated learning solution to provide smart advertising as a classification problem which uses ensemble methods and logistic regression as internal (local) models and meta-heuristic algorithms for federated learning aggregation. As part of the experiments, we prove this technology on a real data set with more than one million registers, and show the efficiency in terms of enhanced accuracy and improved training and response speed.

Smart advertising creates awareness about some offer with a more direct, personalized and interactive focus. In this area, AROUND is a social network aimed at providing smart advertising to suggest appealing business to their customers and friends. The AROUND system is supported by a sophisticated recommender system, which considers not only the customers historical behaviours, but also their current mood and accurate location. In such smart recommendation systems, the response time for the personalized advertising is critical for a successful users’ quality of experience. In this research work we first evaluate the current performance of the AROUND system in terms of processing and communication times considering that, nowadays, this social network has more than 3 million users. The current implementation of the system relies on the deployment of a network of beacons, and uses a domestic cloud provider as the main infrastructure. We show that when the number of concurrent requests becomes too high, the response time faces some limitations. In order to address this issue, we discuss several alternatives, and propose the use of an edge-based strategy as a solution for fast response time. In the experimental section, we measure the performance of the AROUND system, both in our current infrastructure at the cloud and with an edge-based approach, and show the additional advantages of leveraging the edge-based strategy even in the case of overloading the cloud capacity.

Smart cities are becoming popular and, with them, a plethora of resources is emerging turning into a massive concentration of computing devices. In such environments, the deployment of a smart resources management system is envisioned as an extraordinary opportunity to provide almost unlimited computing and networking capabilities obtained by putting together a vast set of nearby devices, hence offering high performance computing while benefiting from low latency networking. In this scenario, where millions of highly heterogeneous devices are available, a key challenge is to select the set of devices suited the most to carry out some specific tasks. In this paper, we present a performance estimation model intended to find an optimal solution for load distribution over massive heterogeneous systems, considering the effects of both, distributed computing and network overhead. The current implementation is in a preliminary stage, considering only fully parallel applications. Trials conducted considering several hardware configurations, with either homogeneous or heterogeneous systems, show the fact that the predicted performance matches the one obtained by the experimental executions with an almost negligible deviation. These initial results encourage us to continue developing the model for more complex and realistic applications.

There are a myriad of computing resources in the World, most of which are underused. Efficiently using some of these devices raises several interesting opportunities. For instance, devices are spread everywhere, so locality can be deeply exploited. Furthermore, combining the computational capacity of all, or some, of them may conform a virtual cluster more powerful than any current data center. And, finally, by using resources already there avoids the need for a specific investment on specific infrastructures, as well as significantly reduces the operational costs. In this paper we propose a research architecture for global resources management which aims at exploiting the availability of this vast amount of heterogeneous resources at the edge of the network. We have designed the Progressive Mapping System (PMS) as a system that keeps track of the available resources and, upon an application launching, finds the best devices configuration for optimal application execution, considering both, the application performance requirements and the resources availability and performance features, while guaranteeing the Quality of Service (QoS). The PMS implements a graph of devices where nodes describe their performance features and edges describe their geographic distance as well as communication capabilities. Given an application description, which includes some performance description and quality of service requirements, the PMS system selects a subset of the global graph by solving a mixed integer quadratically constrained programming formulation, which finds the appropriate set of nodes for optimal application execution performance. One of the novelties of the PMS system is that it considers the geographic positioning of the devices and the application locality requirements to deeper exploiting locality issues. The PMS system is yet in a preliminary implementation stage. In this paper we describe the system overview, the architectural design, and discuss some research challenges which are critical for an effective PMS implementation.

A vast amount of data is constantly being produced in the world from several sources, including the IoT at smart cities. Such complex universe of digital data is being set up through these daily accumulated fresh data over other historical repositories. This data can then be used in different forms and then be reused in further processes, hence, drawing the life cycle of data. Conventionally, the smart city resources management are based on cloud models where sensors data are collected to prepare a centralized and rich set of open data. Cloud-based frameworks provide several benefits, consisting of their ubiquity and (almost) unlimited resources capacity. But accessing data from the cloud results in large network traffics, high latencies and are not recommended for real-time solutions. In order to solve this, fog computing has been defined as a promising technology. It uses devices at the edge to perform closer computation and, therefore, reduces network traffic, decreases latencies, while enhancing security levels. In this paper we present the Data Preservation block, as part of our hierarchical fog-to-cloud F2C data management architecture. This model has the advantages of both fog and cloud technologies, as it allows reduced latencies for critical applications while being able to use the high computing capabilities of cloud technology. Furthermore, we have estimated the amount of collected data to be stored in the storage media, from fog to cloud layers, at the Barcelona smart city, and shown the tremendous advantages of such data organization.

Fog-to-Cloud (F2C) is an emerging architecture intended to manage the resources continuum from far datacenters up to the near edge, putting together the cloud and fog concepts. It aims to obtain both, an efficient utilization of the entire set of available resources and an optimal execution of highly demanding services, supported by the deployment of highly skilled IoT devices at the edge. Many research efforts are pushing for the creation of a widely accepted F2C framework architecture aimed at controlling and managing the resources integrating the F2C network in a hierarchical and distributed fashion. To that end however, many research challenges remain unsolved. This paper puts the focus on one of these challenges, particularly the identity management and proposes a novel strategy that, based on fragmenting the full resource identifier, uses portions of the full resource name to identify the resources in the network without losing the uniqueness property. The presented results show that by using fragments instead of the full identifier, the lookup time and database size in the F2C aggregator nodes are both notably reduced, what undoubtedly enables a fast resource identification.

Traditional smart city resources management rely on cloud based solutions to provide a centralized and rich set of open data. The advantages of cloud based frameworks are their ubiquity, (almost) unlimited resources capacity, cost efficiency, as well as elasticity. However, accessing data from the cloud implies large network traffic, high data latencies, and higher security risks. Alternatively, fog computing emerges as a promising technology to absorb these inconveniences. The use of devices at the edge provides closer computing facilities, reduces network traffic and latencies, and improves security. We have defined a new framework for data management in the context of smart city through a global fog to cloud management architecture; in this paper we present the data acquisition block. As a first experiment we estimate the network traffic during data collection, and compare it with a traditional real system. We also show the effectiveness of some basic data aggregation techniques in the model, such as redundant data elimination and data compression.

The recent deployment of novel network concepts, such as M2M communication or IoT, has undoubtedly stimulated the placement of a new set of services, leveraging both centralized resources in Cloud Data Centers and distributed resources shared by devices at the edge of the network. Moreover, Fog Computing has been recently proposed having as one of its main assets the reduction of service response time, further enabling the deployment of real-time services. Albeit QoS-aware network researches have been originally focused on data plane issues, the successful deployment of real-time services, demanding very low delay on the allocation of distributed resources, depends on the assessment of the impact of controlling decisions on QoS. Recently, Fog-to-Cloud (F2C) computing has been proposed as a hierarchical layered-architecture relying on a coordinated and distributed management of both Fog and Cloud resources, enabling the distributed and parallel allocation of resources at distinct layers, thus suitably mapping services demands into resources availability. In this paper, we assess the layered management architecture in F2C systems, taking into account its distributed nature. Preliminary results show the tradeoff observed regarding controllers capacity, number of controllers, and number of controller layers in the F2C architecture.

A huge amount of data is constantly being produced in the world. Data coming from the IoT, from scientific simulations, or from any other field of the eScience, are accumulated over historical data sets and set up the seed for future Big Data processing, with the final goal to generate added value and discover knowledge. In such computing processes, data are the main resource; however, organizing and managing data during their entire life cycle becomes a complex research topic. As part of this, Data LifeCycle (DLC) models have been proposed to efficiently organize large and complex data sets, from creation to consumption, in any field, and any scale, for an effective data usage and big data exploitation.

There is a vast amount of data being generated every day in the world, coming from a variety of sources, with different formats, quality levels, etc. This new data, together with the archived historical data, constitute the seed for future knowledge discovery and value generation in several fields of eScience. Discovering value from data is a complex computing process where data is the key resource, not only during its processing, but also during its entire life cycle. However, there is still a huge concern about how to organize and manage this data in all fields, and at all scales, for efficient usage and exploitation during all data life cycles. Although several specific Data LifeCycle (DLC) models have been recently defined for particular scenarios, we argue that there is no global and comprehensive DLC framework to be widely used in different fields. For this reason, in this paper we present and describe a comprehensive scenario agnostic Data LifeCycle (COSA-DLC) model successfully addressing all challenges included in the 6Vs, namely Value, Volume, Variety, Velocity, Variability and Veracity, not tailored to any specific environment, but easy to be adapted to fit the requirements of any particular field. We conclude that a comprehensive scenario agnostic DLC model provides several advantages, such as facilitating global data organization and integration, easing the adaptation to any kind of scenario, guaranteeing good quality data levels, and helping save design time and efforts for the research and industrial communities.

This paper presents a tool developed to help engineers to design and develop sustainable projects. The tool has been designed to introduce and evaluate the sustainability of engineering projects in general, but here we show its application to assess the final project of an engineering degree. This tool is a guide for students to introduce and estimate the sustainability of their projects, but it also helps teachers to assess them. The tool is based on the Socratic Methodology and consists of a matrix where each cell contains several questions that students must consider during the project development and which they must answer in their project report. A positive or negative mark is assigned to every cell, and the sum of all marks states the project sustainability. However, the result is not as simplistic as a final number, but a descriptive sustainability analysis where questions are answered and every mark justified. A pilot test with some students has obtained good results, but the first Final Degree Project using this methodology will be read in July 2016.

The introduction of sustainability skills into higher education curricula is a natural effect of the increasing importance of sustainability in our daily lives. Topics like green computing, sustainable design or environmental engineering have become part of the knowledge required by today's engineers. Furthermore, we strongly believe that the introduction of this skill will eventually enable future engineers to develop sustainable products, services and projects. The Final Year Project is the last academic stage facing students and a step towards their future professional engineering projects. As such, it constitutes a rehearsal for their professional future and an ideal opportunity for reflecting on whether their Final Year Project is sustainable or not, and to what extent. It also provides a good tool for reviewing the lessons learned about sustainability during the degree course and for applying them in a holistic and integrated way. In this paper, we present a guide that allows both students and advisors to think carefully about the sustainability of engineering projects, in particular the Final Year Project.

In this paper, we present a methodology for Final Year Project (FYP) monitoring and assessment that considers the inclusion of the professional skills required in the particular engineering degree. This proper monitoring and clear evaluation framework provides the student with valuable support for the project implementation as well as for improving the quality of the projects, thereby reducing the academic drop-out rate. The proposed methodology has been implemented at the Barcelona School of Informatics at the Universität Politècnica de Catalunya - BarcelonaTech. The FYP is structured around three milestones: project definition, project monitoring and project completion. Skills are assigned to each milestone according to the tasks required in that phase, and a list of indicators is defined for each phase. The evaluation criteria for each indicator at each phase are specified in a rubric, and are made public both to students and teachers. Thus, the FYP includes an exhaustive evaluation method distributed throughout the whole project implementation, thereby facilitating project organization for the student as well as providing a clear and homogeneous assessment framework. The methodology for the FYP organization, assessment and evaluation was launched and piloted over two semesters. We believe the experience to be general in the sense that it has been conducted as part of an ICT engineering degree, but may easily be extended to any other engineering degree.

In the context of the European Higher Education Area, curriculum design needs to be based on the defined competencies of each degree programs, including both domain-specific and professional competencies. In this educational context, developing students' professional skills poses a new challenge we need to face. The present work proposes a model to globally develop professional skills in an Engineering degree program. Based on competency maps, this model allows careful analysis, revision and iteration for an effective integration of professional skills. We define each competency in terms of "dimensions" (or sub-skills), which are further defined according to three-level objectives. Competency maps are built showing the specific graded objectives, which allows to integrate them most finely into degree subjects. A global competency map is also designed including the objectives to be achieved throughout the degree. This global map becomes a useful tool for curriculum designers and coordinators. It allows them to optimize the workload, and to make adjustments most effectively, helping students develop the defined competencies as a global comprehensive experience. To illustrate our model, we explain how it has been implemented to integrate "Communication skills" into subjects, and how the model has been applied to assess "Appropriate attitude towards work" skills.

This paper presents a proposal to develop “Sustainability and Social Commitment" skills (SSC) in engineering degree curricula. The proposal is based on providing general education in SSC to teachers (in particular, the relationship between SSC and their field of knowledge) and SSC-related material for each subject, as well as developing learning and assessment methods for these skills. Unlike other proposals advocating the existence of a few specific subjects that include SSC, the authors propose to integrate these skills into different subjects throughout the curriculum. The existence of a global mechanism for the coordination and assessment of SSC skills is also proposed. Finally, the paper explains the introduction of SSC into the Barcelona School of Informatics Computing Engineering Degree at the Technical University of Catalonia (UPC- BarcelonaTECH), providing examples of how to deal with SSC within the subjects of the degree. While the experience is based on a Computing Degree, the ideas and methodology can be extended to other technical studies.

The Java language has rapidly become widespread and it is being used to implement a broad range of applications, including applications with high resource requirements. For this reason, it is important to evaluate the suitability of the Java environment to execute such applications. This paper presents an evaluation of the effects of memory management in the context of memory intensive Java applications executed on a virtual memory system. The goal of this work is to detect the most critical memory management issues for Java applications performance. We measure the overhead that each memory management task adds to the application execution, and we determine which part of this overhead is due to the memory access pattern of the application and which part is due to the interaction between the different memory management tasks.

Parallel architectures with physically distributed memory provide a cost-effective scalability to solve many large scale scientific problems. However, these systems are very difficult to program and tune. In these systems, the choice of a good data mapping and parallelization strategy can dramatically improve the efficiency of the resulting program. In this paper, we present a framework for automatic data mapping in the context of distributed memory multiprocessor systems. The framework is based on a new approach that allows the alignment, distribution, and redistribution problems to be solved together using a single graph representation. The Communication Parallelism Graph (CPG) is the structure that holds symbolic information about the potential data movement and parallelism inherent to the whole program. The CPG is then particularized for a given problem size and target system and used to find a minimal cost path through the graph using a general purpose linear 0-1 integer programming solver. The data layout strategy generated is optimal according to our current cost and compilation models.

Data distribution is one of the key aspects that a parallelizing compiler for a distributed memory architecture should consider, in order to get efficiency from the system. The cost of accessing local and remote data can be one or several orders of magnitude different, and this can dramatically affect performance. In this paper, we present a novel approach to automatically perform static data distribution. All the constraints related to parallelism and data movement are contained in a single data structure, the Communication-Parallelism Graph (CPG). The problem is solved using a linear 0-1 integer programming model and solver. In this paper we present the solution for one-dimensional array distributions, although its extension to multi-dimensional array distributions is also outlined. The solution is static in the sense that the layout of the arrays does not change during the execution of the program. We also show the feasibility of using this approach to solve the problem in terms of compilation time and quality of the solutions generated.