Despliegue óptimo de redes de distribución eléctricas soterradas usando métodos metaheurísticos y simulación

This document presents a planning model that allows optimizing the deployment of underground electrification networks for distribution considering the number of users simultaneously connected to a transformer. We present a model based on a heuristic process that seeks to reduce costs by using the resources required for a minimum cost routing on a geo-referenced scenario. The model is scalable because it allows the population density of the studied georeferenced area to be varied, that is, it adjusts to the use of resources required for different population quantities. Additionally, a simulation process is presented, articulated to the planning model using the Cymdist software, contemplating elements of a real underground electrification network, in order to verify voltage problems, failures, overloads, etc. The obtained results allow to quickly diagnose the possible deployment and routing options of underground networks for distribution, warning to decrease the times for deployment of new networks, in addition the work successfully explores the optimality principle and makes the heuristic process computationally useful. Finally, the proposal provides a road map with a view to the optimal planning of underground electrification networks for distribution.Este documento presenta un modelo de planeación que permite optimizar el despliegue de redes de electrificación soterradas para distribución considerando la cantidad de usuarios conectados simultáneamente a un transformador. Se presenta un modelo basado en un proceso heurístico que busca reducir costes por uso de recursos requeridos para un enrutamiento de mínimo costo sobre un escenario georreferenciado. El modelo es escalable pues permite que se varíe la densidad poblacional del área georreferenciada estudiada, es decir, se ajusta al uso de recursos requeridos para diferentes cantidades poblacionales. Adicionalmente se presenta un proceso de simulación articulado al modelo de planeación mediante el software Cymdist, contemplando elementos de una red de electrificación soterrada real, con la finalidad de verificar problemas de tensión, fallos, sobrecargas, etc. Los resultados obtenidos permiten diagnosticar rápidamente las posibles opciones de despliegue y enrutamiento de redes soterradas para distribución, advirtiendo disminuir los tiempos por despliegue de nuevas redes, además el trabajo explora con éxito el principio de optimalidad y hace que el proceso heurístico sea computacionalmente útil. Finalmente, la propuesta brinda un mapa de ruta con visión hacia la óptima planeación de redes de electrificación soterradas para distribución

Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

Scalable Task Schedulers for Many-Core Architectures

This thesis develops schedulers for many-cores with different optimization objectives. The proposed schedulers are designed to be scale up as the number of cores in many-cores increase while continuing to provide guarantees on the quality of the schedule