Sectorization is a well-established method of increasing the capacity of telecommunicationnetworks. With modern Active Antenna Systems (AAS) comes the abilityto change sectorization order dynamically, both in horizontal and vertical plane.The purpose of this thesis is to investigate when (and what type of) sectorizationis benficial. A theoretical analysis as well as simulations are performed in orderto determine which quantities to look at when making the decision to apply sectorization.Based on the conclusions from these investigations, a self-optimizingalgorithm that only turns on sectorization when it increases network performanceis developed and evaluated. It is shown that large gains can be achieved by onlyturning on sectorization when the right conditions are met. Further, we show thatadditional gains can be seen if antenna parameters such as downtilt and distributionof transmission power between sectors are set properly. Self-optimizingalgorithms for tuning of these parameters are developed and evaluated as well.NyckelordKeywords

Le Benchmarking des systèmes client-serveur implique des infrastructures techniques réparties complexes, dont la gestion nécessite une approche autonomique. Cette gestion s'appuie sur une suite d'étapes, observation, analyse et rétroaction, qui correspond au principe d'une boucle de contrôle autonome. Des travaux antérieurs dans le domaine du test de performances ont montré comment introduire des fonctionnalités de test autonome par le biais d'une injection de charge auto-régulée. L'objectif de cette thèse est de suivre cette démarche de calcul autonome (autonomic computing) en y introduisant des fonctionnalités d'optimisation autonome. On peut ainsi obtenir automatiquement des résultats de benchmarks fiables et comparables, mettant en oeuvre l'ensemble des étapes de self-benchmarking. Notre contribution est double. D'une part, nous proposons un algorithme original pour l'optimisation dans un contexte de test de performance, qui vise à diminuer le nombre de solutions potentielles à tester, moyennant une hypothèse sur la forme de la fonction qui lie la valeur des paramètres à la performance mesurée. Cet algorithme est indépendant du système à optimiser. Il manipule des paramètres entiers, dont les valeurs sont comprises dans un intervalle donné, avec une granularité de valeur donnée. D'autre part, nous montrons une approche architecturale à composants et une organisation du benchmark automatique en plusieurs boucles de contrôle autonomes (détection de saturation, injection de charge, calcul d'optimisation), coordonnées de manière faiblement couplée via un mode de communication asynchrone de type publication-souscription. Complétant un canevas logiciel à composants pour l'injection de charge auto-régulée, nous y ajoutons des composants pour reparamétrer et redémarrer automatiquement le système à optimiser.Deux séries d'expérimentations ont été menées pour valider notre dispositif d'auto-optimisation. La première série concerne une application web de type achat en ligne, déployée sur un serveur d'application JavaEE. La seconde série concerne une application à trois tiers effectifs (WEB, métier (EJB JOnAS) et base de données) clusterSample. Les trois tiers sont sur des machines physiques distinctes. / Benchmarking client-server systems involves complex, distributed technical infrastructures, whose management deserves an autonomic approach. It also relies on observation, analysis and feedback steps that closely matches the autonomic control loop principle. While previous works in performance testing have shown how to introduce autonomic load testing features through self-regulated load injection, the goal of this thesis is to follow this approach of autonomic computing to introduce self-optimization features in this architecture to obtain reliable and comparable benchmark results, and to achieve the fully principle of Self-benchmarking.Our contribution is twofold. From the algorithmic point of view, we propose an original optimization algorithm in the context of performance testing. This algorithm is divided into two parts. The first one concerns the overall level, i.e. the control of the performance index evolution, based on global parameters setting of the system. The second part concerns the search for the optimum when only one parameter is modified. From the software architecture point of view, we complete the Fractal component-based architecture, containing several autonomic control loops (saturation, injection, optimization computing) and we implement the coordination principle between these loops by asynchronous messages according to the publish-subscribe communication paradigm. To apply a given parameters setting on the system under test, we introduced new components Configurators to support the setting of parameters before starting the test process. It may also be necessary to restart all or part of the system to optimize to ensure that the new setting is effectively taken into account. We introduced components Starters to cover this need in a specific way for each system.To validate our self-optimization framework, two types of campaigns have been conducted onto the servers of Orange Labs in Meylan and the servers of the LISTIC Laboratory of the University of Savoie in Polytech Annecy-Chambéry (Annecy le Vieux). The first one is a WEB online shopping application deployed on a Java EE application server JonAS. The second one is a three-tiers application (WEB, business (EJB JOnAS) and data base) clusterSample. The three tiers are in three separate machines.

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Energy efficiency of IT infrastructures has been a well-discussed research topic for several decades. The resulting approaches include hardware optimizations, resource management in operating systems, network protocols, and many more. The approach the authors present in this chapter is a self-optimization technique for IT infrastructures, which takes hard- and software components as well as users of software applications into account. It is able to ensure minimal energy consumption for a user request along with a set of non-functional requirements (e.g., the refresh rate of a data extraction tool). To optimize the ratio between utility of end users and the cost in terms of energy consumption, the system needs inherent variability leading to differentiated energy profiles and mechanisms to reconfigure the system at runtime. The authors present their approach called Energy Auto-Tuning (EAT) comprised of these mechanisms and an architecture which automatically tunes the energy efficiency of IT systems.

Energy efficiency is gaining more and more importance, since well-known ecological reasons lead to rising energy costs. In consequence, energy consumption is now also an important economical criterion. Energy consumption of single hardware resources has been thoroughly optimized for years. Now software becomes the major target of energy optimization. In this paper we introduce an approach called energy auto tuning(EAT), which optimizes energy efficiency of software systems running on multiple resources. The optimization of more than one resource leads to higher energy savings, because communication costs can be taken into account. E.g., if two components run on the same resource, the communication costs are likely to be less, compared to be running on different resources. The best results can be achieved in heterogeneous environments as different resource characteristics enlarge the synergy effects gainable by our optimization technique. EAT software systems derive all possible distributions of themselves on a given set of hardware resources and reconfigure themselves to achieve the lowest energy consumption possible at any time. In this paper we describe our software architecture to implement EAT.

Energy efficiency of IT infrastructures has been a well-discussed research topic for several decades. The resulting approaches include hardware optimizations, resource management in operating systems, network protocols, and many more. The approach the authors present in this chapter is a self-optimization technique for IT infrastructures, which takes hard- and software components as well as users of software applications into account. It is able to ensure minimal energy consumption for a user request along with a set of non-functional requirements (e.g., the refresh rate of a data extraction tool). To optimize the ratio between utility of end users and the cost in terms of energy consumption, the system needs inherent variability leading to differentiated energy profiles and mechanisms to reconfigure the system at runtime. The authors present their approach called Energy Auto-Tuning (EAT) comprised of these mechanisms and an architecture which automatically tunes the energy efficiency of IT systems.

Energy efficiency is gaining more and more importance, since well-known ecological reasons lead to rising energy costs. In consequence, energy consumption is now also an important economical criterion. Energy consumption of single hardware resources has been thoroughly optimized for years. Now software becomes the major target of energy optimization. In this paper we introduce an approach called energy auto tuning(EAT), which optimizes energy efficiency of software systems running on multiple resources. The optimization of more than one resource leads to higher energy savings, because communication costs can be taken into account. E.g., if two components run on the same resource, the communication costs are likely to be less, compared to be running on different resources. The best results can be achieved in heterogeneous environments as different resource characteristics enlarge the synergy effects gainable by our optimization technique. EAT software systems derive all possible distributions of themselves on a given set of hardware resources and reconfigure themselves to achieve the lowest energy consumption possible at any time. In this paper we describe our software architecture to implement EAT.

La motivation principale de cette thèse est de faire face à l'accroissement de la complexité des systèmes informatiques, qui, dans un futur proche ( de l'ordre de quelques années) risque fort d'être le principal frein à leur évolution et à leur développement. Aujourd'hui la tendance s'inverse et le coût de gestion humaine dépasse le coût des infrastructures matérielles et logicielles. De plus, l'administration manuelle de grands systèmes (applications distribuées, réseaux de capteurs, équipements réseaux) est non seulement lente mais aussi sujette à de nombreuses erreurs humaines. Un des domaines de recherche émergent est celui de l'informatique autonomique qui a pour but de rendre ces systèmes auto-gérés. Nous proposons une approche qui permet de décrire des politiques de gestion autonomiques de haut niveau. Ces politiques permettent au système d'assurer quatre propriétés fondamentales de l'auto-gestion: l'auto-guérison, l'auto-configuration, l'auto-protection et l'auto-optimisation. Nos contributions portent sur la spécification de diagrammes de description de politiques de gestion autonomiques appelés (S)PDD "(Sensor) Policy Description Diagrams". Ces diagrammes sont implémentés dans le gestionnaire autonomique TUNe et l'approche a été validée sur de nombreux systèmes: simulation électromagnétique répartie sur grille de calcul, réseaux de capteurs SunSPOT, répartiteur de calcul DIET. Une deuxième partie présente une modélisation mathématique de l’auto-optimisation pour un « datacenter ». Nous introduisons un problème de minimisation d’un critère intégrant d’une part la consommation électrique des équipements du réseau du « datacenter » et d’autre part la qualité de service des applications déployées sur le « datacenter ». Une heuristique permet de prendre en compte les contraintes dues aux fonctions de routage utilisées. / The main challenge of this thesis is to cope with the growing complexity of IT systems. In a near future (mainly the next few years) this complexity will prevent new developments and system evolutions. Today the trend is reversing and the managing costs are overtaking the hardware and software costs. Moreover, the manual administration of large systems (distributed applications, sensor networks, and network equipment) is not only slow but error-prone. An emerging research field called autonomic computing tries to bring up self-managed systems. We introduce an approach that enable the description of high level autonomic management policies. These policies allow the system to ensure four fundamental properties for self-management: self-healing, self-self-configuring, self-protecting and self-optimizing. We specify autonomic management Policy Description Diagrams (PDD) and implement them in Toulouse University Network (TUNe). We validated our approach on many systems: electromagnetic simulations distributed on computer grids (grid’5000), wireless sensor networks with SunSPOTs and the computing scheduler DIET. A second part of this thesis presents a mathematical modeling for self-optimizing datacenters. We introduce a minimization problem with a criterion integrating both the electrical consumption of the datacenter networking equipment and the quality of service of the deployed applications. A heuristic takes into account the routing functions used on the network.