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Recent advances in 5G and edge computing enable rapid development and deployment of edge-cloud systems, which are ideal for delay-sensitive machine learning (ML) applications such as autonomous driving and smart city. Distributed ML jobs often need to train a large model with enormous datasets, which can only be handled by deploying a distributed set of workers in an edge-cloud system. One common approach is to employ a parameter server (PS) architecture, in which training is carried out at multiple workers, while PSs are used for aggregation and model updates. In this architecture, one of the fundamental challenges is how to dispatch ML jobs to workers and PSs such that the average job completion time (JCT) can be minimized. In this work, we propose a novel online preemptive scheduling framework to decide the location and the execution time window of concurrent workers and PSs upon each job arrival. Specifically, our proposed scheduling framework consists of: i) a job dispatching and scheduling algorithm that assigns each ML job to workers and decides the schedule to train each data chunk; ii) a PS assignment algorithm that determines the placement of PS. We prove theoretically that our proposed algorithm is $D_{max}(1+1/\epsilon)$ -competitive with $(1 + \epsilon)$ -speed augmentation, where $D_{max}$ is the maximal number of data chunks in any job. Extensive testbed experiments and trace-driven simulations show that our algorithm can reduce the average JCT by up to 30% compared with state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Surveillance of children born to women with human immunodeficiency virus (HIV) infection at five pediatric regional centers assessed times and patterns of clinical recognition in these children. Regional HIV seroprevalences among childbearing women were used to assess the proportion of identified children born to HIV-infected women. In total, 415 children with perinatal HIV exposure were identified. Early age at first HIV evaluation was significantly associated with maternal intravenous drug use (3.2 vs. 7.2 months for other or unknown maternal risk, P = .01), birth county with population > 500,000 (3.5 vs. 8.2 months for population < or = 500,000, P = .003), and hospital with routine HIV screening of pregnant women (0.1 vs. 8.8 months for no screening, P = .006). Race did not correlate with age at first evaluation. Using maternal HIV seroprevalence rates for 1988-1991, 34%-50% of the expected number of infants born to HIV-infected women were in clinical care. Perception of increased maternal risk for HIV infection was associated with early clinical recognition of infants of HIV-infected women. [ABSTRACT FROM AUTHOR]