Short Papers of the 11th Conference on Cloud Computing Conference, Big Data & Emerging Topics (JCC-BD&ET 2023)

Compilación de los short papers presentados en las 11vas Jornadas de Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET2023), llevadas a cabo en modalidad híbrida durante junio de 2023 y organizadas por el Instituto de Investigación en Informática LIDI (III-LIDI) y la Secretaría de Posgrado de la Facultad de Informática de la UNLP en colaboración con universidades de Argentina y del exterior.Facultad de Informátic

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future

On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

[EN]The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human- Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and datadriven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques

On Data-driven systems analyzing, supporting and enhancing users’ interaction and experience

Tesis doctoral en inglés y resumen extendido en español[EN] The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human-Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and data-driven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques

Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial

Este documento contiene el proyecto docente e investigador del candidato Germán Moltó Martínez presentado como requisito para el concurso de acceso a plazas de Cuerpos Docentes Universitarios. Concretamente, el documento se centra en el concurso para la plaza 6708 de Catedrático de Universidad en el área de Ciencia de la Computación en el Departamento de Sistemas Informáticos y Computación de la Universitat Politécnica de València. La plaza está adscrita a la Escola Técnica Superior d'Enginyeria Informàtica y tiene como perfil las asignaturas "Infraestructuras de Cloud Público" y "Estructuras de Datos y Algoritmos".También se incluye el Historial Académico, Docente e Investigador, así como la presentación usada durante la defensa.Germán Moltó Martínez (2022). Proyecto Docente e Investigador, Trabajo Original de Investigación y Presentación de la Defensa, preparado por Germán Moltó para concursar a la plaza de Catedrático de Universidad, concurso 082/22, plaza 6708, área de Ciencia de la Computación e Inteligencia Artificial. http://hdl.handle.net/10251/18903

A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems

Producción CientíficaIn recent years, the number of embedded computing devices connected to the Internet has exponentially increased. At the same time, new applications are becoming more complex and computationally demanding, which can be a problem for devices, especially when they are battery powered. In this context, the concepts of computation offloading and edge computing, which allow applications to be fully or partially offloaded and executed on servers close to the devices in the network, have arisen and received increasing attention. Then, the design of algorithms to make the decision of which applications or tasks should be offloaded, and where to execute them, is crucial. One of the options that has been gaining momentum lately is the use of Reinforcement Learning (RL) and, in particular, Deep Reinforcement Learning (DRL), which enables learning optimal or near-optimal offloading policies adapted to each particular scenario. Although the use of RL techniques to solve the computation offloading problem in edge systems has been covered by some surveys, it has been done in a limited way. For example, some surveys have analysed the use of RL to solve various networking problems, with computation offloading being one of them, but not the primary focus. Other surveys, on the other hand, have reviewed techniques to solve the computation offloading problem, being RL just one of the approaches considered. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for computation offloading in edge computing system. We present a comprehensive and detailed survey, where we analyse and classify the research papers in terms of use cases, network and edge computing architectures, objectives, RL algorithms, decision-making approaches, and time-varying characteristics considered in the analysed scenarios. In particular, we include a series of tables to help researchers identify relevant papers based on specific features, and analyse which scenarios and techniques are most frequently considered in the literature. Finally, this survey identifies a number of research challenges, future directions and areas for further study.Consejería de Educación de la Junta de Castilla y León y FEDER (VA231P20)Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42, PID2021-124463OBI00 y RED2018-102585-T, financiados por MCIN/AEI/10.13039/501100011033

Interdisciplinary Film & Digital Media 2015 APR Self-Study & Documents

UNM Interdisciplinary Film & Digital Media APR self-study report, review team report, response to review report, and initial action plan for Spring 2015, fulfilling requirements of the Higher Learning Commission. IFDM was absorbed by the Cinematic Arts Department following this review