Multi-objective Robust Static Mapping of Independent Tasks on Grids

We study the problem of efficiently allocating incoming independent tasks onto the resources of a Grid system. Typically, it is assumed that the estimated time to compute each task on every machine is known. We are making the same assumption in this work, but we allow the existence of inaccuracies in these values. Our schedule will be robust versus such inaccuracies, ensuring that even when the estimated time to compute all the tasks is increased by a given percentage, the makespan of the schedule (i.e., the time when the last machine finishes its tasks) will not grow behind that percentage. We propose a new multi-objective definition of the problem, optimizing at the same time the makespan of the schedule and its robustness. Four well-known multi-objective evolutionary algorithms are used to find competitive results to the new problem. Finally, a new population initialization method for scheduling problems is proposed, leading to more efficient and accurate algorithms

Energy-aware scheduling in distributed computing systems

Distributed computing systems, such as data centers, are key for supporting modern computing demands. However, the energy consumption of data centers has become a major concern over the last decade. Worldwide energy consumption in 2012 was estimated to be around 270 TWh, and grim forecasts predict it will quadruple by 2030. Maximizing energy efficiency while also maximizing computing efficiency is a major challenge for modern data centers. This work addresses this challenge by scheduling the operation of modern data centers, considering a multi-objective approach for simultaneously optimizing both efficiency objectives. Multiple data center scenarios are studied, such as scheduling a single data center and scheduling a federation of several geographically-distributed data centers. Mathematical models are formulated for each scenario, considering the modeling of their most relevant components such as computing resources, computing workload, cooling system, networking, and green energy generators, among others. A set of accurate heuristic and metaheuristic algorithms are designed for addressing the scheduling problem. These scheduling algorithms are comprehensively studied, and compared with each other, using statistical tools to evaluate their efficacy when addressing realistic workloads and scenarios. Experimental results show the designed scheduling algorithms are able to significantly increase the energy efficiency of data centers when compared to traditional scheduling methods, while providing a diverse set of trade-off solutions regarding the computing efficiency of the data center. These results confirm the effectiveness of the proposed algorithmic approaches for data center infrastructures.Los sistemas informáticos distribuidos, como los centros de datos, son clave para satisfacer la demanda informática moderna. Sin embargo, su consumo de energético se ha convertido en una gran preocupación. Se estima que mundialmente su consumo energético rondó los 270 TWh en el año 2012, y algunos prevén que este consumo se cuadruplicará para el año 2030. Maximizar simultáneamente la eficiencia energética y computacional de los centros de datos es un desafío crítico. Esta tesis aborda dicho desafío mediante la planificación de la operativa del centro de datos considerando un enfoque multiobjetivo para optimizar simultáneamente ambos objetivos de eficiencia. En esta tesis se estudian múltiples variantes del problema, desde la planificación de un único centro de datos hasta la de una federación de múltiples centros de datos geográficmentea distribuidos. Para esto, se formulan modelos matemáticos para cada variante del problema, modelado sus componentes más relevantes, como: recursos computacionales, carga de trabajo, refrigeración, redes, energía verde, etc. Para resolver el problema de planificación planteado, se diseñan un conjunto de algoritmos heurísticos y metaheurísticos. Estos son estudiados exhaustivamente y su eficiencia es evaluada utilizando una batería de herramientas estadísticas. Los resultados experimentales muestran que los algoritmos de planificación diseñados son capaces de aumentar significativamente la eficiencia energética de un centros de datos en comparación con métodos tradicionales planificación. A su vez, los métodos propuestos proporcionan un conjunto diverso de soluciones con diferente nivel de compromiso respecto a la eficiencia computacional del centro de datos. Estos resultados confirman la eficacia del enfoque algorítmico propuesto

Optimization and Mining Methods for Effective Real-Time Embedded Systems

L’Internet des objets (IoT) est le réseau d’objets interdépendants, comme les voitures autonomes, les appareils électroménagers, les téléphones intelligents et d’autres systèmes embarqués. Ces systèmes embarqués combinent le matériel, le logiciel et la connection réseau permettant le traitement de données à l’aide des puissants centres de données de l’informatique nuagique. Cependant, la croissance exponentielle des applications de l’IoT a remodelé notre croyance sur l’informatique nuagique, et des certitudes durables sur ses capacités ont dû être mises à jour. De nos jours, l’informatique nuagique centralisé et classique rencontre plusieurs défis, tels que la latence du trafic, le temps de réponse et la confidentialité des données. Alors, la tendance dans le traitement des données générées par les dispositifs embarqués interconnectés consiste à faire plus de calcul au niveau du dispositif au bord du réseau. Cette possibilité de faire du traitement local aide à réduire la latence pour les applications temps réel présentant des fortes contraintes temporelles. Aussi, ça permet d’améliorer le traitement des quantités massives de données générées par ces périphériques. Réussir cette transition nécessite la conception de systèmes embarqués de haute performance en explorant efficacement les alternatives de conception (i.e. Exploration efficace de l’espace des solutions), en optimisant la topologie de déploiement des applications temps réel sur des architectures multi-processeurs (i.e. la façon dont le logiciel utilise le matériel) , et des algorithme d’exploration permettant un fonctionnement plus intelligent de ces dispositifs. Des efforts de recherche récents ont conduit à diverses approches automatisées facilitant la conception et l’amélioration du fonctionnement des système embarqués. Cependant, ces techniques existantes présentent plusieurs défis majeurs. Ces défis sont fortement présents sur les systèmes embarqués temps réel. Quatre des principaux défis sont : (1) Le manque de techniques d’exploration de données en ligne permettant l’amélioration des performances des systèmes embarqués. (2) L’utilisation inefficace des ressources informatiques des systèmes multiprocesseurs lors du déploiement de logiciels là dessus ; (3) L’exploration pseudo-aléatoire de l’espace des solutions (4) La sélection de la configuration appropriée à partir de la listes des solutions optimales obtenue.----------ABSTRACT: The Internet of things (IoT) is the network of interrelated devices or objects, such as selfdriving cars, home appliances, smart-phones and other embedded computing systems. It combines hardware, software, and network connectivity enabling data processing using powerful cloud data centers. However, the exponential rise of IoT applications reshaped our belief on the cloud computing, and long-lasting certainties about its capabilities had to be updated. The classical centralized cloud computing is encountering several challenges, such as traffic latency, response time, and data privacy. Thus, the trend in the processing of the generated data of IoT inter-connected embedded devices has shifted towards doing more computation closer to the device in the edge of the network. This possibility to do on-device processing helps to reduce latency for critical real-time applications and better processing of the massive amounts of data being generated by the these devices. Succeeding this transition towards the edge computing requires the design of high-performance embedded systems by efficiently exploring design alternatives (i.e. efficient Design Space Exploration), optimizing the deployment topology of multi-processor based real-time embedded systems (i.e. the way the software utilizes the hardware), and light mining techniques enabling smarter functioning of these devices. Recent research efforts on embedded systems have led to various automated approaches facilitating the design and the improvement of their functioning. However, existing methods and techniques present several major challenges. These challenges are more relevant when it comes to real-time embedded systems. Four of the main challenges are : (1) The lack of online data mining techniques that can enhance embedded computing systems functioning on the fly ; (2) The inefficient usage of computing resources of multi-processor systems when deploying software on ; (3) The pseudo-random exploration of the design space ; (4) The selection of the suitable implementation after performing the otimization process