Energy efficient scheduling for hard real-time systems

Für moderne elektronische Systeme spielt der Energieverbrauch eine immer wichtigere Rolle. Geringer Stromverbrauch und lange Akkulaufzeit sind die wichtigsten Anforderungen bei der Entwicklung, um die Betriebskosten der Geräte zu reduzieren. Auf Systemebene gibt es zwei weit verbreitete Techniken, um den Energieverbrauch zu reduzieren: Dynamic Power Management (DPM) und Dynamic Voltage and Frequency Scaling (DVS). Beide Techniken sind in der Lage, den Trade-off zwischen Systemleistung und Stromverbrauch zu regulieren. Da beide Techniken den Energieverbrauch auf Kosten der Systemleistung reduzieren, sollten sie insbesondere in der Kombination mit Echtzeitsystemen mit Bedacht eingesetzt werden. Um den Energieverbrauch in Echtzeitsystemen zu reduzieren, beschäftigt sich diese Arbeit mit dem Problem der Energieverbrauchsoptimierung mit Hilfe einer kombinierten Anwendung von DPM und DVS. Hiermit wird insbesondere der Aufwand beim Zustandswechsel für DPM und DVS untersucht. Leider ist das betrachtete Optimierungsproblem NP-hart, sodass für seine Lösung keine effizienten Algorithmen existieren. Daher wird in dieser Dissertation ein heuristischer Suchalgorithmus entwickelt, der den Simulated Annealing Algorithmus um spezielle Regeln für die Selektion von Nachbarn erweitert. Darüber hinaus wird eine auf Regression basierte Technik zur Analyse des Verhaltens des vorgestellten Algorithmus erarbeitet. Ferner präsentiert diese Arbeit einen Ansatz zur Onlineausführung des vorgestellten Algorithmus. Dabei besteht die größte Herausforderung darin, dass der heuristische Algorithmus in der Ausführung des Echtzeitsystems integriert werden muss. Dadurch ist das System in der Lage, sich selbstständig an dynamische Veränderungen anzupassen. Noch wichtiger ist jedoch der geführte Nachweis, dass der Laufzeitaufwand der Onlineausführung gering ist.In modern electronic systems, especially in battery-driven devices, energy consumption has clearly become one of the most important design concerns. Low power consumption and long battery life are major development requirements and objectives to reduce system operation cost. From the system-level point of view, there are two widely applied energy saving techniques, Dynamic Power Management (DPM) and Dynamic Voltage and Frequency Scaling (DVS), which are able to adjust the trade-off between system performance and power consumption. Both techniques reduce system power consumption at the cost of performance loss, which is a crucial point in the context of hard real-time systems. To address energy optimization problem, this dissertation studies in detail the combined application of DPM and DVS on both single- and multi-core processor platforms, in particular with non-negligible state switching overhead. Unfortunately, the facing problem is proven to be NP-hard in the strong sense, which indicates non-existence of efficient algorithms. Thus, this work proposes a heuristic search algorithm by extending simulated annealing with neighbor selection guidelines using domain specific information. In addition, a regression based mechanism to predict algorithm run-time behavior is proposed, which in turn is used for quality estimation of a solution and derivation of an efficient termination criterion. Furthermore, this dissertation presents an approach, which is able to run the proposed algorithms in a completely online fashion. Hereby, the main challenge is to integrate the heuristic into the execution of real-time tasks, which is solved by mapping iterations of the algorithm to hyper periods of the task execution. In doing so, a system becomes self-adaptive to dynamic changes. More importantly, it can be shown that the run-time overhead of this approach is provably low.Tag der Verteidigung: 20.12.2013Paderborn, Univ., Diss., 201

Big data analytics in healthcare: A cloud based framework for generating insights

With exabytes of data being generated from genome sequencing, a whole new science behind genomic big data has emerged. As technology improves, the cost of sequencing a human genome has gone down considerably increasing the number of genomes being sequenced. Huge amounts of genomic data along with a vast variety of clinical data cannot be handled using existing frameworks and techniques. It is to be efficiently stored in a warehouse where a number of things have to be taken into account. Firstly, the genome data is to be integrated effectively and correctly with clinical data. The other data sources along with their formats have to be identified. Required data is then extracted from these other sources (such as clinical datasets) and integrated with the genome. The main challenge here is to be able to handle the integration complexity as a large number of datasets are being integrated with huge amounts of genome. Secondly, since the data is captured at disparate locations individually by clinicians and scientists, it brings the challenge of data consistency. It has to be made sure that the data consistency is not compromised as it is passed along the warehouse. Checks have to be put in place to make sure the data remains consistent from start to finish. Thirdly, to carry this out effectively, the data infrastructure has to be in the correct order. How frequently the data is accessed plays a crucial role here. Data in frequent use will be handled differently than data which is not in frequent use. Lastly, efficient browsing mechanisms have to put in place to allow the data to be quickly retrieved. The data is then iteratively analysed to get meaningful insights. The challenge here is to perform analysis very quickly. Cloud Computing plays an important role as it is used to provide scalability.N/