Cost-aware multi data-center bulk transfers in the cloud from a customer-side perspective

Many cloud applications (e.g., data backup and replication, video distribution) require dissemination of large volumes of data from a source data-center to multiple geographically distributed data-centers. Given the high costs of wide-area bandwidth, the overall cost of inter-data-center communication is a major concern in such scenarios. While previous works have focused on optimizing the costs of bulk transfer, most of them use the charging models of Internet service providers, typically based on the 95th percentile of bandwidth consumption. However, public Cloud Service Providers (CSP) follow very different models to charge their customers. First, the cost for transmission is flat and depends on the location of the source and receiver data-centers. Second, CSPs offer discounts once customer transfers exceed certain volume thresholds per data-center. We present a systematic framework, CloudMPcast, that exploits these two aspects of cloud pricing schemes. CloudMPcast constructs overlay distribution trees for bulk-data transfer that both optimizes dollar costs of distribution, and ensures end-to-end data transfer times are not affected. CloudMPCast monitors TCP throughputs between data-centers and only proposes alternative trees that respect original transfer times. After an extensive measurement study, the cost savings range from 10 to 60 percent for both Azure and EC2 infrastructures, which potentially translates to millions of dollars a year assuming realistic demandsThis material is based upon work supported in part by the National Science Foundation (NSF) under Award No.1162333, . J. L. Garc ıa-Dorado is thankful for the financial support of the Jos e Castillejo Program (CAS12/00057

SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis

[EN]Introduction: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme implementation. Methods and analysis: We will perform a cross-sectional survey of randomly selected residents of Salamanca (Spain). 2400 individuals stratified by age and sex and by place of residence (rural and urban) will be studied. The variables to analyse will be obtained from the clinical history, different surveys including social status, Mediterranean diet, functional capacity, ECG, echocardiogram, VASERA and biochemical as well as genetic analysis. Ethics and dissemination: The study has been approved by the ethical committee of the healthcare community. All study participants will sign an informed consent for participation in the study. The results of this study will allow the understanding of the relationship between the different influencing factors and their relative importance weights in the development of structural heart disease

Body silhouettes as a tool to reflect obesity in the past

Life course data on obesity may enrich the quality of epidemiologic studies analysing health consequences of obesity. However, achieving such data may require substantial resources. We investigated the use of body silhouettes in adults as a tool to reflect obesity in the past. We used large population-based samples to analyse to what extent self-reported body silhouettes correlated with the previously measured (9-23 years) body mass index (BMI) from both measured (European Community Respiratory Health Survey, N = 3 041) and self-reported (Respiratory Health In Northern Europe study, N = 3 410) height and weight. We calculated Spearman correlation between BMI and body silhouettes and ROC-curve analyses for identifying obesity (BMI ≥30) at ages 30 and 45 years. Spearman correlations between measured BMI age 30 (±2y) or 45 (±2y) and body silhouettes in women and men were between 0.62-0.66 and correlations for self-reported BMI were between 0.58-0.70. The area under the curve for identification of obesity at age 30 using body silhouettes vs previously measured BMI at age 30 (±2y) was 0.92 (95% CI 0.87, 0.97) and 0.85 (95% CI 0.75, 0.95) in women and men, respectively; for previously self-reported BMI, 0.92 (95% CI 0.88, 0.95) and 0.90 (95% CI 0.85, 0.96). Our study suggests that body silhouettes are a useful epidemiological tool, enabling retrospective differentiation of obesity and non-obesity in adult women and men

Body silhouettes as a tool to reflect obesity in the past

<div><p>Life course data on obesity may enrich the quality of epidemiologic studies analysing health consequences of obesity. However, achieving such data may require substantial resources.</p><p>We investigated the use of body silhouettes in adults as a tool to reflect obesity in the past. We used large population-based samples to analyse to what extent self-reported body silhouettes correlated with the previously measured (9–23 years) body mass index (BMI) from both measured (European Community Respiratory Health Survey, N = 3 041) and self-reported (Respiratory Health In Northern Europe study, N = 3 410) height and weight. We calculated Spearman correlation between BMI and body silhouettes and ROC-curve analyses for identifying obesity (BMI ≥30) at ages 30 and 45 years. Spearman correlations between measured BMI age 30 (±2y) or 45 (±2y) and body silhouettes in women and men were between 0.62–0.66 and correlations for self-reported BMI were between 0.58–0.70. The area under the curve for identification of obesity at age 30 using body silhouettes <i>vs</i> previously measured BMI at age 30 (±2y) was 0.92 (95% CI 0.87, 0.97) and 0.85 (95% CI 0.75, 0.95) in women and men, respectively; for previously self-reported BMI, 0.92 (95% CI 0.88, 0.95) and 0.90 (95% CI 0.85, 0.96). Our study suggests that body silhouettes are a useful epidemiological tool, enabling retrospective differentiation of obesity and non-obesity in adult women and men.</p></div