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Experience has shown that a priori created static resource allocation plans are vulnerable to runtime deviations and hence often become uneconomic or highly exceed a predefined soft deadline. The assumption of constant task execution times during allocation planning is even more unlikely in a cloud environment where virtualized resources vary in performance. Revising the initially created resource allocation plan at runtime allows the scheduler to react on deviations between planning and execution. Such an adaptive rescheduling of a many-task application workflow is only feasible, when the planning time can be handled efficiently at runtime. In this paper, we present the static low-complexity resource allocation planning algorithm (LCP) applicable to efficiently schedule many-task scientific application workflows on cloud resources of different capabilities. The benefits of the presented algorithm are benchmarked against alternative approaches. The benchmark results show that LCP is not only able to compete against higher complexity algorithms in terms of planned costs and planned makespan but also outperforms them significantly by magnitudes of 2 to 160 in terms of required planning time. Hence, LCP is superior in terms of practical usability where low planning time is essential such as in our targeted online rescheduling scenario.
Neurophysiologisch ist das nicht alles zu erklären : Nahtoderfahrungen aus wissenschaftlicher Sicht
(2017)
Neue Perspektiven für die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation
(2020)
Neue Kläranlage Rostock - kombinierter Einsatz von Belebungs- und Aufstromfiltrationsverfahren
(1996)
Wie sieht das unbemannte Flugzeug von Übermorgen aus? Dieser Frage stellen sich Forscher an der Fachhochschule Aachen. Die weltweit rasant fortschreitende Entwicklung des Marktes für unbemannte Fluggeräte (UAVs - „Unmanned Aerial Vehicles“) bietet großes Potenzial für Wachstum und Wertschöpfung. Unbemannte fliegende Systeme können – für bestimmte Anwendungsgebiete – wesentlich günstiger, kleiner und effizienter ausgelegt werden als bemannte Lösungen. Dabei sind sich viele Unternehmen über das mögliche Potential dieser Technologie noch gar nicht bewusst.
Kundenanforderungen an Netzwerke haben sich in den vergangenen Jahren stark verändert. Mit NFV und SDN sind Unternehmen technisch in der Lage, diesen gerecht zu werden. Die Provider stehen jedoch vor großen Herausforderungen: Insbesondere Produkte und Prozesse müssen angepasst und agiler werden, um die Stärken von NFV und SDN zum Kundenvorteil auszuspielen.
Netzgestaltung und Straßenraumentwurf in gewachsenen Wohngebieten mit geringer Flächenverfügbarkeit
(2008)
Intensive poultry operation systems emit a considerable volume of inorganic and organic matter in the surrounding environment. Monitoring cleaning properties of exhaust air cleaning systems and to detect small but significant changes in emission characteristics during a fattening cycle is important for both emission and fattening process control. In the present study, we evaluated the potential of near-infrared spectroscopy (NIRS) combined with chemometric techniques as a monitoring tool of exhaust air from poultry operation systems. To generate a high-quality data set for evaluation, the exhaust air of two poultry houses was sampled by applying state-of-the-art filter sampling protocols. The two stables were identical except for one crucial difference, the presence or absence of an exhaust air cleaning system. In total, twenty-one exhaust air samples were collected at the two sites to monitor spectral differences caused by the cleaning device, and to follow changes in exhaust air characteristics during a fattening period. The total dust load was analyzed by gravimetric determination and included as a response variable in multivariate data analysis. The filter samples were directly measured with NIR spectroscopy. Principal component analysis (PCA), linear discriminant analysis (LDA), and factor analysis (FA) were effective in classifying the NIR exhaust air spectra according to fattening day and origin. The results indicate that the dust load and the composition of exhaust air (inorganic or organic matter) substantially influence the NIR spectral patterns. In conclusion, NIR spectroscopy as a tool is a promising and very rapid way to detect differences between exhaust air samples based on still not clearly defined circumstances triggered during a fattening period and the availability of an exhaust air cleaning system.