We propose a linear regression method and a maximum likelihood technique for estimating the service demands of requests based on measurement of their response times instead of their CPU utilization. Our approach does not require server instrumentation or sampling, thus simplifying the parameterization of performance models. The benefit of this approach is further highlighted when utilization measurement is difficult or unreliable, such as in virtualized systems or for services controlled by third parties. Both experimental results from an industrial ERP system and sensitivity analyses based on simulations indicate that the proposed methods are often much more effective for service demand estimation than popular utilization based linear regression methods. In particular, the maximum likelihood approach is found to be typically two to five times more accurate than utilization based regression, thus suggesting that estimating service demands from response times can help in improving performance model parameterization. * Stephan Kraft is also affiliated with Queen'
Resource allocation in the cloud is usually driven by performance predictions, such as estimates of the future incoming load to the servers or of the quality-of-service (QoS) offered by applications to end users. In this context, characterizing web workload fluctuations in an accurate way is fundamental to understand how to provision cloud resources under time-varying traffic intensities. In this paper, we investigate the Markovian Arrival Processes (MAP) and the related MAP/MAP/1 queueing model as a tool for performance prediction of servers deployed in the cloud. MAPs are a special class of Markov models used as a compact description of the time-varying characteristics of workloads. In addition, MAPs can fit heavy-tail distributions, that are common in HTTP traffic, and can be easily integrated within analytical queueing models to efficiently predict system performance without simulating. By comparison with tracedriven simulation, we observe that existing techniques for MAP parameterization from HTTP log files often lead to inaccurate performance predictions. We then define a maximum likelihood method for fitting MAP parameters based on data commonly available in Apache log files, and a new technique to cope with batch arrivals, which are notoriously difficult to model accurately. Numerical experiments demonstrate the accuracy of our approach for performance prediction of web systems.
To assess whether the spread of infection with HIV can be reduced by changes in behaviour among groups most at risk because of their sexual practices sexual behaviour was monitored among 1050 homosexual men tested for HIV infection at a genitourinary medicine clinic in west London from November 1984 to September 1987. Four cohorts, defined by date of presentation, were studied by questionnaire at their presentation, and blood samples were analysed.Between the first and last cohorts there was a considerable fall in the proportion reporting casual relationships (291/329 (88%) v 107/213 (50%)) and high risk activities, such as anoreceptive intercourse with casual partners (262/291 (90%) v 74/106 (70%)), with the greatest changes occurring before the government information campaign began in 1986. Nevertheless, half of the men in the last cohort studied reported having casual partners. Multiple logistic regression showed that behavioural risk factors for HIV infection most closely resembled those for hepatitis B and that previous sexually transmitted diseases (syphilis, hepatitis B, and anogenital herpes) were themselves independent risk factors. A history of syphilis ranked above anoreceptive intercourse as the strongest predictor of HIV infection. Actively bisexual men showed a much lower prevalence of HIV infection (3/57, 5%) than exclusively homosexual men (113/375, 30%).Sexual behaviour among homosexual men changed during the period studied, and the incidence of HIV infection fell, although more education programmes directed at homosexual men are needed to re-emphasise the dangers of infection.
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