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Today's IP backbones are provisioned to provide excellent performance in terms of loss, delay and availability. However, performance degradation and service disruption are likely in the case of failure, such as fiber cuts, router crashes, etc. In this paper, we investigate the occurence of failures in Sprint's IP backbone and their potential impact on emerging services such as Voice-over-IP (VoIP). We first examine the frequency and duration of failure events derived from IS-IS routing updates collected from three different points in the Sprint IP backbone. We observe that link failures occur as part of everyday operation, and the majority of them are short-lived (less than 10 minutes). We also discuss various statistics such as the distribution of inter-failure time, distribution of link failure durations, etc. which are essential for constructing a realistic link failure model. Next, we present an analysis of routing and service reconvergence time during a controlled link failure scenario in our backbone. Our results indicate that disruption to packet forwarding after link failures depends not only on routing protocol dynamics, but also on the design of routers' architectures and control planes. Thus our resuits offer insights into two basic components for defining network-wide availability, which we consider a more appropriate metric for service-level agreements to support emerging applications.

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Future healthcare systems will rely heavily on clinical decision support systems (CDSS) to improve the decision-making processes of clinicians. To explore the design of future CDSS, we developed a research-focused CDSS for the management of patients in the intensive care unit that leverages Internet of Things devices capable of collecting streaming physiologic data from ventilators and other medical devices. We then created machine learning models that could analyze the collected physiologic data to determine if the ventilator was delivering potentially harmful therapy and if a deadly respiratory condition, acute respiratory distress syndrome (ARDS), was present. We also present work to aggregate these models into a mobile application that can provide

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