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Long-term execution of scientific applications often leads to dynamic workloads and varying application requirements. When the execution uses resources provisioned from IaaS clouds, and thus consumption-related payment, efficient and online scheduling algorithms must be found. Portfolio scheduling, which selects dynamically a suitable policy from a broad portfolio, may provide a solution to this problem. However, selecting online the right policy from possibly tens of alternatives remains challenging. In this work, we introduce an abstract model to explore this selection problem. Based on the model, we present a comprehensive portfolio scheduler that includes tens of provisioning and allocation policies. We propose an algorithm that can enlarge the chance of selecting the best policy in limited time, possibly online. Through trace-based simulation, we evaluate various aspects of our portfolio scheduler, and find performance improvements from 7% to 100% in comparison with the best constituent policies and high improvement for bursty workloads.

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SUMMARY Due to its advantages of cost‐effectiveness, on‐demand provisioning and easy for sharing, cloud computing has grown in popularity with the research community for deploying scientific applications such as workflows. Although such interests continue growing and scientific workflows are widely deployed in collaborative cloud environments that consist of a number of data centers, there is an urgent need for exploiting strategies which can place application datasets across globally distributed data centers and schedule tasks according to the data layout to reduce both latency and makespan for workflow execution. In this paper, by utilizing dependencies among datasets and tasks, we propose an efficient data and task coscheduling strategy that can place input datasets in a load balance way and meanwhile, group the mostly related datasets and tasks together. Moreover, data staging is used to overlap task execution with data transmission in order to shorten the start time of tasks. We build a simulation environment on Tianhe supercomputer for evaluating the proposed strategy and run simulations by random and realistic workflows. The results demonstrate that the proposed strategy can effectively improve scheduling performance while reducing the total volume of data transfer across data centers. Concurrency and Computation: Practice and Experience, 2013.© 2013 Wiley Periodicals, Inc.

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Applying finite-element vertical discretization to a mass-based non-hydrostatic kernel has proved difficult due to the constraints of vertical operators. This article proposes a novel hybrid finite-element vertical discretization method for a semi-implicit mass-based nonhydrostatic kernel, which integrates a finite-differential scheme and a finite-element scheme. In the hybrid method, the finite-differential scheme which satisfies the set of constraints is applied to the linear part, while a cubic finite-element scheme with high-order accuracy is applied to the non-linear part. Furthermore, to improve the accuracy of the linear part, an enlarged set of vertical levels is applied to the differential scheme. This set of vertical levels is only used to solve semi-implicit equations, and has no impact on the grid point calculation and spectral transformations. A series of 2D idealized test cases are conducted to verify the stability and the accuracy of our new method.

In order to identify and describe the effectiveness of transdermal testosterone pretreatment on poor ovarian responders, MEDLINE, EMBASE, the Cochrane library and the Chinese biomedical database were searched for randomized controlled trials (RCTs). Three RCTs, which compared the outcomes of female pretreatment with transdermal testosterone prior to in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) with those of control groups, were included in the present review. The three RCTs enrolled a total of 221 randomized subjects. The meta-analysis revealed that females who received transdermal testosterone treatment prior to their IVF/ICSI cycle had a two-fold increase in live birth rate [risk ratio (RR)=2.01, 95% confidence interval (CI) 1.03–3.91], clinical pregnancy rate (RR=2.09, 95% CI 1.14–3.81) and a significantly more oocyte retrieved [mean difference (MD)=1.36, 95% CI 0.82–1.90]. The current findings provide evidence that pretreatment with transdermal testosterone may improve the clinical outcomes for poor ovarian responders undergoing IVF/ICSI. However, the results should be interpreted with caution due to the small sample size of the studies used and the heterogeneities. Further good quality RCTs would be needed to reach further conclusions.

A general iteration formula of variational iteration method (VIM) for fractional heat- and wave-like equations with variable coefficients is derived. Compared with previous work, the Lagrange multiplier of the method is identified in a more accurate way by employing Laplace’s transform of fractional order. The fractional derivative is considered in Jumarie’s sense. The results are more accurate than those obtained by classical VIM and the same as ADM. It is shown that the proposed iteration formula is efficient and simple.