Performance of middleware based architectures: a quantitative approach

Most of today's E-business applications on the Internet are built upon middleware-based architectures. For service providers offering these applications performance is essential: less-than-acceptable performance levels may lead to customer churn, and thus loss of revenue, and as such directly affect the company's competitive edge. This raises the critical need for service providers to be able to predict and control performance. In this paper we demonstrate the usefulness of a quantitative modeling approach to analyze and predict the performance of middleware-based applications. To this end, we develop a quantitative performance model of middleware architectures based on CORBA, the de-facto standard for object middleware. A particular feature of the model is that it explicitly takes into account priority mechanisms that handle the access to the processors among the different threads. To validate the model we have compared performance predictions from simulation runs with results from lab experiments for a variety of parameter settings. The results show that (1) the inclusion of priority mechanisms in the model leads to a significant improvement of the accuracy of the performance predictions based on the model, and (2) a quantitative modeling approach to assess and predict the performance of middleware-based applications is very promising

Optimal Sampling Strategies for Therapeutic Drug Monitoring of First-Line Tuberculosis Drugs in Patients with Tuberculosis

BACKGROUND: The 24-h area under the concentration-time curve (AUC24)/minimal inhibitory concentration ratio is the best predictive pharmacokinetic/pharmacodynamic (PK/PD) parameter of the efficacy of first-line anti-tuberculosis (TB) drugs. An optimal sampling strategy (OSS) is useful for accurately estimating AUC24; however, OSS has not been developed in the fed state or in the early phase of treatment for first-line anti-TB drugs. METHODS: An OSS for the prediction of AUC24 of isoniazid, rifampicin, ethambutol and pyrazinamide was developed for TB patients starting treatment. A prospective, randomized, crossover trial was performed during the first 3 days of treatment in which first-line anti-TB drugs were administered either intravenously or in fasting or fed conditions. The PK data were used to develop OSS with best subset selection multiple linear regression. The OSS was internally validated using a jackknife analysis and externally validated with other patients from different ethnicities and in a steady state of treatment. RESULTS: OSS using time points of 2, 4 and 8 h post-dose performed best. Bias was < 5% and imprecision was < 15% for all drugs except ethambutol in the fed condition. External validation showed that OSS2-4-8 cannot be used for rifampicin in steady state conditions. CONCLUSION: OSS at 2, 4 and 8 h post-dose enabled an accurate and precise prediction of AUC24 values of first-line anti-TB drugs in this population. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02121314)

A clinical prediction model for long-term functional outcome after traumatic spinal cord injury based on acute clinical and imaging factors.

To improve clinicians\u27 ability to predict outcome after spinal cord injury (SCI) and to help classify patients within clinical trials, we have created a novel prediction model relating acute clinical and imaging information to functional outcome at 1 year. Data were obtained from two large prospective SCI datasets. Functional independence measure (FIM) motor score at 1 year follow-up was the primary outcome, and functional independence (score ≥ 6 for each FIM motor item) was the secondary outcome. A linear regression model was created with the primary outcome modeled relative to clinical and imaging predictors obtained within 3 days of injury. A logistic model was then created using the dichotomized secondary outcome and the same predictor variables. Model validation was performed using a bootstrap resampling procedure. Of 729 patients, 376 met the inclusion criteria. The mean FIM motor score at 1 year was 62.9 (±28.6). Better functional status was predicted by less severe initial American Spinal Injury Association (ASIA) Impairment Scale grade, and by an ASIA motor score \u3e50 at admission. In contrast, older age and magnetic resonance imaging (MRI) signal characteristics consistent with spinal cord edema or hemorrhage predicted worse functional outcome. The linear model predicting FIM motor score demonstrated an R-square of 0.52 in the original dataset, and 0.52 (95% CI 0.52,0.53) across the 200 bootstraps. Functional independence was achieved by 148 patients (39.4%). For the logistic model, the area under the curve was 0.93 in the original dataset, and 0.92 (95% CI 0.92,0.93) across the bootstraps, indicating excellent predictive discrimination. These models will have important clinical impact to guide decision making and to counsel patients and families

Optimal Sampling Strategies for Therapeutic Drug Monitoring of First-Line Tuberculosis Drugs in Patients with Tuberculosis

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