Establishing healthy sleep-wake patterns early in infancy is vitally important as sleep problems can persist. Behavioural sleep interventions such as the parental presence procedure are well established and have been found to improve infant sleep as determined by parent report. The exact nature of this improvement is, however, unclear. Sleep consolidation, sleep-state organisation, and self-soothing are thought likely to change after intervention; however, no known research has comprehensively determined which of these variables change as infant sleep changes in response to intervention. Three participants aged between 7 to 11 months who met the criteria for Infant Sleep Disturbance (ISD) were referred by a Health Centre and the parental presence behavioural sleep intervention was implemented. Parental report and videosomonography (VSG) data were used to measure sleep before and after intervention. While parental report is limited in that parents can only report what they can hear and/or see, VSG offers a tool that can be used to measure sleep-state organisation, state changes, and periods when the infant is awake and quiet. The present research found that infants’ sleep became more consolidated resulting in fewer sleep-wake transitions and night wakings. Infants who had difficulties initiating sleep on their own also demonstrated decrease in Sleep Onset Delay (SOD). Furthermore, infants were found to sleep through a greater number of sleep-state transitions and sleep for a greater duration of time before waking. Collectively this research provides some evidence that changing parental behaviours to those that promote self-initiation through self-soothing and consistency, can change sleep-state organisation and improve self-soothing.

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.