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Optical burst switching (OBS) has attracted interest as a transport network architecture for the future optical Inter-net. As OBS relies on statistical multiplexing efficient contention resolution is a key issue in order to achieve a low burst loss probability. Basically, contentions can be resolved by wavelength conversion, deflection routing and delaying the burst in a fiber delay line or a combination of these schemes. This paper compares the basic and combined contention resolution strategies in two reference core network scenarios with respect to burst loss probability and end-to-end transfer delay. We show that the effectiveness of those contention resolution schemes highly depends on the load offered to the network and the dimensioning of specific nodes and links. For high load, contention resolution schemes applying deflection routing have an end-to-end transfer time increase in the order of l o d 0 %depending on the scheme.

SummaryObjectives: This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Methods: Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. Results: There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Conclusion: Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation.

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