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Politiques de gestion d'énergie et de température dans les systèmes informatiques / Scheduling algorithms for energy and thermal management in computer systemsLetsios, Dimitrios 22 October 2013 (has links)
La gestion de la consommation d’énergie et de la température est devenue un enjeu crucial dans les systèmes informatiques. En effet, un grand centre de données consomme autant d’électricité qu’une ville et les processeurs modernes atteignent des températures importantes dégradant ainsi leurs performances et leur fiabilité. Dans cette thèse, nous étudions différents problèmes d’ordonnancement prenant en compte la consommation d’énergie et la température des processeurs en se focalisant sur leur complexité et leur approximabilité. Pour cela, nous utilisons le modèle de Yao et al. (1995) (modèle de variation de vitesse) pour la gestion d’énergie et le modèle de Chrobak et al. (2008) pour la gestion de la température. / Nowadays, the enegy consumption and the heat dissipation of computing environments have emerged as crucial issues. Indeed, large data centers consume as muse electricity as a city while modern processors attain high temperatures degrading their performance and decreasing their reliability.. In this thesis, we study various energy and temperature aware scheduling problems and we focus on their complexity and approximability. A dominant technique for saving energy is by prosper scheduling of the jobs through the operating system combined with appropriate scaling of the processor's speed. This technique is referred to as speed scaling in the literature and its theoretical study was initiated by Yao, Demers and Shenker (FOCS'1995). In order to manage the thermal behavior of a computing device, we adaopt the approach of Chrobak, Dürr, Hurand and Robert (AAIM'2008). The main assumption is that some jobs are more CPU intensive than others and more heat is generated during their execution. Moreover, the cooling of a computing device occurs by introducing appropriate idle periods.
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Global scheduling on temperature-constrained multiprocessor real-time systemsKoo, Ja-Ryeong 10 October 2008 (has links)
In this thesis, we study temperature-constrained multiprocessor real-time systems,
where real-time guarantees must be met without exceeding safe temperature levels
within the processors. We focus on Pfair scheduling algorithms, especially ERfair
scheduling scheme (a work-conserving extension to Pfair scheduling) as our main
multiprocessor real-time scheduling methodology. Then, we study the benefits of
simple reactive speed scaling as described in the real-time multiprocessor systems.
In this thesis, in support of the temperature-awareness, we extend the applicability
of the reactive speed scaling to global scheduling schemes for multiprocessors. We
propose temperature-aware scheduling and processor selection schemes motivated by
existing (thermally non-optimal) ERfair scheduling in order to reduce thermal stress
and therefore increase the processor utilization. Then, we show that the proposed
algorithm and reactive scheme can enhance the processor utilization compared with
any constant speed scheme on real-time multiprocessor systems. Additionally, we
show how the maximum schedulable utilization (MSU) for partitioning heuristics can
be determined on the temperature-constrained multiprocessor real-time systems.
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Scheduling algorithms for saving energy and balancing loadAntoniadis, Antonios 16 August 2012 (has links)
Diese Arbeit beschäftigt sich mit Scheduling von Tasks in Computersystemen. Wir untersuchen sowohl die in neueren Arbeiten betrachtete Zielfunktion zur Energieminimierung als auch die klassische Zielfunktion zur Lastbalancierung auf mehreren Prozessoren. Beim Speed-Scaling mit Sleep-State darf ein Prozessor, der zu jedem Zeitpunkt seine Geschwindigkeit anpassen kann, auch in einen Schlafmodus übergehen. Unser Ziel ist es, den Energieverbrauch zu minimieren. Wir zeigen die NP-Härte des Problems und klären somit den Komplexitätsstatus. Wir beweisen eine untere Schranke für die Approximationsgüte für eine spezielle natürliche Klasse von Schedules. Ferner entwickeln wir eine Familie von Algorithmen, die gute Approximationsfaktoren liefert, und zeigen, dass diese sogar Lösungen liefert, die optimal für die zuvor erwähnte Klasse von Schedules sind. Anschließend widmen wir unsere Aufmerksamkeit dem folgenden Termin-basierten Scheduling-Problem. Es seien mehrere Prozessoren gegeben, wobei jeder einzelne Prozessor zu jedem Zeitpunkt seine Geschwindigkeit anpassen kann. Ziel ist es wie zuvor, den Energieverbrauch des erzeugten Schedules zu minimieren. Für den Offline-Fall entwickeln wir einen optimalen Polynomialzeit-Algorithmus. Für das Online-Problem erweitern wir die zwei bekannten Ein-Prozessor-Algorithmen Optimal Available und Average Rate. Wir zeigen, dass diese den gleichen bzw. einen um die additive Konstante von eins vergrößerten kompetiven Faktor haben. Bei der Lastbalancierung auf mehreren Prozessoren betrachten wir Offline-Load-Balancing auf identischen Maschinen. Unser Ziel ist es, die Current-Load für temporäre Tasks mit identischem Gewicht zu minimieren. Wir zeigen, dass eine Lösung mit maximaler Imbalance von eins immer existiert und entwickeln einen effizienten Algorithmus, der solche Lösungen liefert. Zum Schluss beweisen wir die NP-Härte von zwei Verallgemeinerungen des Problems. / This thesis studies problems of scheduling tasks in computing environments. We consider both the modern objective function of minimizing energy consumption, and the classical objective of balancing load across machines. We first investigate offline deadline-based scheduling in the setting of a single variable-speed processor that is equipped with a sleep state. The objective is that of minimizing the total energy consumption. Apart from settling the complexity of the problem by showing its NP-hardness, we provide a lower bound of 2 for general convex power functions, and a particular natural class of schedules. We also present an algorithmic framework for designing good approximation algorithms. Furthermore, we give tight bounds for the aforementioned particular class of schedules. We then focus on the multiprocessor setting where each processor has the ability to vary its speed. We first study the offline problem and show that optimal schedules can be computed efficiently in polynomial time. Regarding the online problem and a natural class of power functions, we extend the two well-known single-processor algorithms Optimal Available and Average Rate. We prove that Optimal Available has the same competitive ratio as in the single-processor case. For Average Rate we show a competitive factor that increases by an additive constant of one compared to the single-processor result. With respect to load balancing, we consider offline load balancing on identical machines, with the objective of minimizing the current load, for temporary unit-weight jobs. The problem can be seen as coloring n intervals with k colors, such that for each point on the line, the maximal difference between the number of intervals of any two colors is minimal. We prove that a coloring with maximal difference at most one is always possible, and develop a fast polynomial-time algorithm for generating such a coloring. Lastly, we prove that two generalizations of the problem are NP-hard.
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