Randomized routing of virtual machines in IaaS data centers.
Ontology highlight
ABSTRACT:
SUBMITTER: Khani H
PROVIDER: S-EPMC7924677 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe widespread popularity of genomic applications is threatened by the "bioinformatics bottleneck" resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly.ResultsWe present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers.ConclusionsAlthough bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.
| S-EPMC3197577 | biostudies-literature
Project description:For the edge computing network, whether the end-to-end delay satisfies the delay constraint of the task is critical, especially for delay-sensitive tasks. Virtual machine (VM) migration improves the robustness of the network, whereas it also causes service downtime and increases the end-to-end delay. To study the influence of failure, migration, and recovery of VMs, we define three states for the VMs in an edge server and build a continuous-time Markov chain (CTMC). Then, we develop a matrix-geometric method and a first passage time method to obtain the VMs timely reliability (VTR) and the end-to-end timely reliability (ETR). The numerical results are verified by simulation based on OMNeT++. Results show that VTR is a monotonic function of the migration rate and the number of VMs. However, in some cases, the increase in task VMs (TVMs) may conversely decrease VTR, since more TVMs also brings about more failures in a given time. Moreover, we find that there is a trade-off between TVMs and backup VMs (BVMs) when the total number of VMs is limited. Our findings may shed light on understanding the impact of VM migration on end-to-end delay and designing a more reliable edge computing network for delay-sensitive applications.
| S-EPMC7795631 | biostudies-literature
Project description:Emergency medical services routing of patients with acute stroke to designated centers may increase the proportion of patients receiving care at facilities meeting national standards and augment recruitment for prehospital stroke research.We analyzed consecutive patients enrolled within 2 hours of symptom onset in a prehospital stroke trial, before and after regional Los Angeles County Emergency Medical Services implementation of preferentially routing patients with acute stroke to approved stroke centers (ASCs). From January 2005 to mid-November 2009, patients were transported to the nearest emergency department, whereas from mid-November 2009 to December 2012, patients were preferentially transported to first 9, and eventually 29, ASCs.There were 863 subjects enrolled before and 764 after emergency medical service preferential routing, with implementation leading to an increase in the proportion cared for at an ASC from 10% to 91% (P<0.0001), with a slight decrease in paramedic on-scene to emergency department arrival time (34.5 [SD, 9.1] minutes versus 33.5 [SD, 10.3] minutes; P=0.045). The effects of routing were immediate and included an increase in proportion of receiving ASC care (from 17% to 88%; P<0.001) and a greater number of enrollments (18.6% increase) when comparing 12 months before and after regional stroke system implementation.The establishment of a regionalized emergency medical services system of acute stroke care dramatically increased the proportion of patients with acute stroke cared for at ASCs, from 1 in 10 to >9 in 10, with no clinically significant increase in prehospital care times and enhanced recruitment of patients into a prehospital treatment trial.URL: http://www.clinicaltrials.gov. Unique identifier: NCT00059332.
| S-EPMC4920547 | biostudies-other
Project description:BackgroundThe COVID-19 pandemic has had negative psychosocial impacts on young children; teachers in childcare centers continue to be overwhelmed by how to address the downstream psychological effects children are experiencing. This randomized controlled trial will study the role of a community-based, childcare center-support system in improving resilience and mitigating the long-term impacts of COVID-19 on children's development.MethodsThis study will be modeled on a successful Early Childhood Mental Health Consultation (ECMHC) intervention which utilizes mental health consultants to deliver a Jump Start+: COVID 19 Support (JS + CS) virtual toolkit to childcare centers in Miami-Dade County via a Kubi robot. The toolkit comprises four strength-based strategies likely to be effective in improving resiliency following disasters: Safety Planning, Effective Communication, Adult Self-Care, and Trauma-Informed Behavior Support. Our first aim will utilize a cluster randomized trial to examine the effectiveness of JS + CS on improving the psychosocial functioning of young children, as compared to an obesity prevention intervention control group. Children will be followed at 6, 12, 18, and 24 months. The second aim will examine the mechanisms that contribute to effective uptake by teachers of the JS + CS support strategies on child outcomes. The third aim will explore implementation barriers/facilitators as well as potential societal contextual factors (e.g., vaccine uptake) to help centers serving disproportionately affected minority communities recover from and prepare for future crises.ConclusionThis design will inform the refinement and scaling of JS + CS and generalize impacts to other childcare center interventions in the context of disasters.
| S-EPMC9694403 | biostudies-literature
Project description:The entangled network structure of the quantum Internet formulates a high complexity routing space that is hard to explore. Scalable routing is a routing method that can determine an optimal routing at particular subnetwork conditions in the quantum Internet to perform a high-performance and low-complexity routing in the entangled structure. Here, we define a method for routing space exploration and scalable routing in the quantum Internet. We prove that scalable routing allows a compact and efficient routing in the entangled networks of the quantum Internet.
| S-EPMC7367878 | biostudies-literature
Project description:Groundwater models have evolved to encompass more aspects of the water cycle, but the incorporation of realistic boundary conditions representing surface water remains time-consuming and error-prone. We present two Python packages that robustly automate this process using readily available hydrography data as the primary input. SFRmaker creates input for the MODFLOW SFR package, while Linesink-maker creates linesink string input for the GFLOW analytic element program. These programs can reduce weeks or even months of manual effort to a few minutes of execution time, and carry the added advantages of reduced potential for error, improved reproducibility and facilitation of step-wise modeling through reduced dependency on a particular conceptual model or discretization. Two real-world examples at the county to multi-state scales are presented.
| S-EPMC8518817 | biostudies-literature
Project description:BackgroundRecent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.ResultsWe apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.ConclusionsOur approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.
| S-EPMC3726335 | biostudies-literature
Project description:The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled "Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data" (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.
| S-EPMC5008053 | biostudies-literature
Project description:Functional and longitudinal data are becoming more and more common in practice. This paper focuses on sparse and irregular longitudinal data with a multicategory response. The predictor consists of sparse and irregular observations, potentially contaminated with measurement errors, on the predictor trajectory. To deal with this type of complicated predictors, we borrow the strength of large margin classifiers in statistical learning for classification of sparse and irregular longitudinal data. In particular, we propose functional robust truncated-hinge-loss support vector machines to perform multicategory classification with the aid of functional principal component analysis.
| S-EPMC3668975 | biostudies-literature
Project description:BackgroundMusculoskeletal conditions account for 16% of global disability, resulting in a negative effect on patients and increasing demand for health care use. Triage directing patients to appropriate level intervention improving health outcomes and efficiency has been prioritized. We developed a musculoskeletal digital assessment routing tool (DART) mobile health (mHealth) system, which requires evaluation prior to implementation. Such innovations are rarely rigorously tested in clinical trials-considered the gold standard for evaluating safety and efficacy. This pilot study is a precursor to a trial assessing DART performance with a physiotherapist-led triage assessment.ObjectiveThe study aims to evaluate trial design, assess procedures, and collect exploratory data to establish the feasibility of delivering an adequately powered, definitive randomized trial, assessing DART safety and efficacy in an NHS primary care setting.MethodsA crossover, noninferiority pilot trial using an integrated knowledge translation approach within a National Health Service England primary care setting. Participants were patients seeking assessment for a musculoskeletal condition, completing a DART assessment and the history-taking element of a face-to-face physiotherapist-led triage in a randomized order. The primary outcome was agreement between DART and physiotherapist triage recommendation. Data allowed analysis of participant recruitment and retention, randomization, blinding, study burden, and potential barriers to intervention delivery. Participant satisfaction was measured using the System Usability Scale.ResultsOver 8 weeks, 129 patients were invited to participate. Of these, 92% (119/129) proceeded to eligibility assessment, with 60% (78/129) meeting the inclusion criteria and being randomized into each intervention arm (39/39). There were no dropouts and data were analyzed for all 78 participants. Agreement between physiotherapist and DART across all participants and all primary triage outcomes was 41% (32/78; 95% CI 22-45), intraclass correlation coefficient 0.37 (95% CI 0.16-0.55), indicating that the reliability of DART was poor to moderate. Feedback from the clinical service team led to an adjusted analysis yielding of 78% (61/78; 95% CI 47-78) and an intraclass correlation coefficient of 0.57 (95% CI 0.40-0.70). Participant satisfaction was measured quantitively using amalgamated System Usability Scale scores (n=78; mean score 84.0; 90% CI +2.94 to -2.94), equating to an "excellent" system. There were no study incidents, and the trial burden was acceptable.ConclusionsPhysiotherapist-DART agreement of 78%, with no adverse triage decisions and high patient satisfaction, was sufficient to conclude DART had the potential to improve the musculoskeletal pathway. Study validity was enhanced by the recruitment of real-world patients and using an integrated knowledge translation approach. Completion of a context-specific consensus process is recommended to provide definitive definitions of safety criteria, range of appropriateness, noninferiority margin, and sample size. This pilot demonstrated an adequately powered definitive trial is feasible, which would provide evidence of DART safety and efficacy, ultimately informing potential for DART implementation.Trial registrationClinicalTrials.gov NCT04904029; http://clinicaltrials.gov/ct2/show/NCT04904029.International registered report identifier (irrid)RR2-10.2196/31541.
| S-EPMC11322692 | biostudies-literature