Resolución de problemas MaxSAT a través de Evolución Diferencial

[Resumen] En este proyecto se ha desarrollado un algoritmo capaz de resolver el problema MaxSAT empleando un algoritmo evolutivo híbrido o memético, que combina el algoritmo evolutivo de Evolución Diferencial con GSAT y RandomWalk, dos heurísticas de búsqueda local específicas de MaxSAT. El algoritmo desarrollado ha sido empleado para resolver benchmarks recientes de la Evaluación MaxSAT 2020. Se ha comparado el funcionamiento del algoritmo con el de los mejores solvers presentados en la Evaluación MaxSAT 2020, alcanzando el estado del arte tanto en la calidad de las soluciones como en el tiempo de cómputo requerido para obtenerlas.[Abstract]In this project, an algorithm capable of solving the MaxSAT problem has been developed using a hybrid evolutionary or memetic algorithm, which combines the evolutionary algorithm of Differential Evolution with GSAT and RandomWalk, two MaxSAT-specific local search heuristics. The algorithm developed has been used to solve recent benchmarks of the MaxSAT Evaluation 2020. The performance of the algorithm has been compared with that of the best solvers presented in the MaxSAT Evaluation 2020, reaching the state of the art both in the quality of the solutions and in the computing time required to obtain them.Traballo fin de grao. Enxeñaría Informática. Curso 2020/202

BDWatchdogFaaS: A Tool for Monitoring and Analysis of Functions-as-a-Service in Cloud Environment

Cursos e Congresos , C-155BDWatchdog is a framework to assist in the in-depth and real-time analysis of the execution of Big Data frameworks and applications. BDWatchdog was originally developed to monitor Hadoop ecosystems deployed on serverless containers, in order to detect bottlenecks and spot certain patterns that frameworks or applications may have. In thisworkwe shift the focus to monitoring serverless functions in the public cloud, by proposing an extension of BDWatchdog which captures, transforms and analyzes logs from both AWS Cloudwatch and Azure Application Insights, which store the logs from AWS Lambda and Azure Functions (respectively), the FaaS (Function-as-a-Service) solutions of the two main public cloud providers, AWS and Azure. The extension, called BDWatchdogFaaS, builds and stores a common model to both providers, allowing to consult, analyze and monitor function logs from AWS and Azure indistinctly. The transformation of logs into the common model is done by a FaaS of the corresponding provider, which in near real-time ingests, processes and sends the data to a common storage. In addition, the data is forwarded to a Power BI dashboard so that the serverless functions can be monitored easilyThis work was funded by the Ministry of Science and Innovation of Spain (ref. PDC2021- 121309-I00/MCIN/AEI/10.13039/501100011033) and by the European Union “NextGenerationEU/ PRTR”. CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Conseller´ıa de Cultura, Educaci´on, Formaci´on Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS