Energy Efficient Scheduling and Routing via Randomized Rounding

We propose a unifying framework based on configuration linear programs and randomized rounding, for different energy optimization problems in the dynamic speed-scaling setting. We apply our framework to various scheduling and routing problems in heterogeneous computing and networking environments. We first consider the energy minimization problem of scheduling a set of jobs on a set of parallel speed scalable processors in a fully heterogeneous setting. For both the preemptive-non-migratory and the preemptive-migratory variants, our approach allows us to obtain solutions of almost the same quality as for the homogeneous environment. By exploiting the result for the preemptive-non-migratory variant, we are able to improve the best known approximation ratio for the single processor non-preemptive problem. Furthermore, we show that our approach allows to obtain a constant-factor approximation algorithm for the power-aware preemptive job shop scheduling problem. Finally, we consider the min-power routing problem where we are given a network modeled by an undirected graph and a set of uniform demands that have to be routed on integral routes from their sources to their destinations so that the energy consumption is minimized. We improve the best known approximation ratio for this problem.Comment: 27 page

Algorithmes exacts et approchés pour des problèmes d'ordonnancement et de placement

Dans cette thèse, nous nous intéressons à la résolution de quelques problèmes d'optimisation combinatoires que nous avons choisi de traiter en deux volets. Dans un premier temps, nous étudions des problèmes d'optimisation issus de l'ordonnancement d'un ensemble de tâches sur des machines de calcul et où on cherche à minimiser l'énergie totale consommée par ces machines tout en préservant une qualité de service acceptable. Dans un deuxième temps, nous traitons deux problèmes d'optimisation classiques à savoir un problème d'ordonnancement dans une architecture de machines parallèles avec des temps de communication, et un problème de placement de données dans des graphes modélisant des réseaux pair-à-pair et visant à minimiser le coût total d'accès aux données.In this thesis, we focus on solving some combinatorial optimization problems that we have chosen to study in two parts. Firstly, we study optimization problems issued from scheduling a set of tasks on computing machines where we seek to minimize the total energy consumed by these machines while maintaining acceptable quality of service. In a second step, we discuss two optimization problems, namely a classical scheduling problem in architecture of parallel machines with communication delays, and a problem of placing data in graphs that represent peer-to-peer networks and the goal is to minimize the total cost of data access.EVRY-Bib. électronique (912289901) / SudocSudocFranceF