Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

A Methodological Framework for Modeling Pavement Maintenance Costs for Projects with Performance-based Contracts

Performance-based maintenance contracts differ significantly from material and method-based contracts that have been traditionally used to maintain roads. Road agencies around the world have moved towards a performance-based contract approach because it offers several advantages like cost saving, better budgeting certainty, better customer satisfaction with better road services and conditions. Payments for the maintenance of road are explicitly linked to the contractor successfully meeting certain clearly defined minimum performance indicators in these contracts. Quantitative evaluation of the cost of performance-based contracts has several difficulties due to the complexity of the pavement deterioration process. Based on a probabilistic analysis of failures of achieving multiple performance criteria over the length of the contract period, an effort has been made to develop a model that is capable of estimating the cost of these performance-based contracts. One of the essential functions of such model is to predict performance of the pavement as accurately as possible. Prediction of future degradation of pavement is done using Markov Chain Process, which requires estimating transition probabilities from previous deterioration rate for similar pavements. Transition probabilities were derived using historical pavement condition rating data, both for predicting pavement deterioration when there is no maintenance, and for predicting pavement improvement when maintenance activities are performed. A methodological framework has been developed to estimate the cost of maintaining road based on multiple performance criteria such as crack, rut and, roughness. The application of the developed model has been demonstrated via a real case study of Miami Dade Expressways (MDX) using pavement condition rating data from Florida Department of Transportation (FDOT) for a typical performance-based asphalt pavement maintenance contract. Results indicated that the pavement performance model developed could predict the pavement deterioration quite accurately. Sensitivity analysis performed shows that the model is very responsive to even slight changes in pavement deterioration rate and performance constraints. It is expected that the use of this model will assist the highway agencies and contractors in arriving at a fair contract value for executing long term performance-based pavement maintenance works.

Integrated sensitivity analysis, calibration, and uncertainty propagation analysis approaches for supporting hydrological modeling

The successful performance of a hydrological model is usually challenged by the quality of the sensitivity analysis, calibration and uncertainty analysis carried out in the modeling exercise and subsequent simulation results. This is especially important under changing climatic conditions where there are more uncertainties associated with climate models and downscaling processes that increase the complexities of the hydrological modeling system. In response to these challenges and to improve the performance of the hydrological models under changing climatic conditions, this research proposed five new methods for supporting hydrological modeling. First, a design of experiment aided sensitivity analysis and parameterization (DOE-SAP) method was proposed to investigate the significant parameters and provide more reliable sensitivity analysis for improving parameterization during hydrological modeling. The better calibration results along with the advanced sensitivity analysis for significant parameters and their interactions were achieved in the case study. Second, a comprehensive uncertainty evaluation scheme was developed to evaluate three uncertainty analysis methods, the sequential uncertainty fitting version 2 (SUFI-2), generalized likelihood uncertainty estimation (GLUE) and Parameter solution (ParaSol) methods. The results showed that the SUFI-2 performed better than the other two methods based on calibration and uncertainty analysis results. The proposed evaluation scheme demonstrated that it is capable of selecting the most suitable uncertainty method for case studies. Third, a novel sequential multi-criteria based calibration and uncertainty analysis (SMC-CUA) method was proposed to improve the efficiency of calibration and uncertainty analysis and control the phenomenon of equifinality. The results showed that the SMC-CUA method was able to provide better uncertainty analysis results with high computational efficiency compared to the SUFI-2 and GLUE methods and control parameter uncertainty and the equifinality effect without sacrificing simulation performance. Fourth, an innovative response based statistical evaluation method (RESEM) was proposed for estimating the uncertainty propagated effects and providing long-term prediction for hydrological responses under changing climatic conditions. By using RESEM, the uncertainty propagated from statistical downscaling to hydrological modeling can be evaluated. Fifth, an integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) was proposed for investigating the effects and contributions of different uncertainty components to the total propagated uncertainty from statistical downscaling. Using ISES-UPA, the uncertainty from statistical downscaling, uncertainty from hydrological modeling, and the total uncertainty from two uncertainty sources can be compared and quantified. The feasibility of all the methods has been tested using hypothetical and real-world case studies. The proposed methods can also be integrated as a hydrological modeling system to better support hydrological studies under changing climatic conditions. The results from the proposed integrated hydrological modeling system can be used as scientific references for decision makers to reduce the potential risk of damages caused by extreme events for long-term water resource management and planning.

Quality planning based on risk assessment

Product quality planning is a fundamental part of quality assurance in manufacturing. It is composed of the distribution of quality aims over each phase in product development and the deployment of quality operations and resources to accomplish these aims. This paper proposes a quality planning methodology based on risk assessment and the planning tasks of product development are translated into evaluation of risk priorities. Firstly, a comprehensive model for quality planning is developed to address the deficiencies of traditional quality function deployment (QFD) based quality planning. Secondly, a novel failure knowledge base (FKB) based method is discussed. Then a mathematical method and algorithm of risk assessment is presented for target decomposition, measure selection, and sequence optimization. Finally, the proposed methodology has been implemented in a web based prototype software system, QQ-Planning, to solve the problem of quality planning regarding the distribution of quality targets and the deployment of quality resources, in such a way that the product requirements are satisfied and the enterprise resources are highly utilized. © Springer-Verlag Berlin Heidelberg 2010.

Modeling 2R regenerator based on High Nonlinear Dispersion Imbalanced Loop Mirror (HN-DILM)

The model of Reshaping and Re-amplification (2R) regenerator based on High Nonlinear Dispersion Imbalanced Loop Mirror (HN-DILM) has been designed to examine its capability to reduce the necessary of fiber loop length and input peak power by deploying High Non linear Fiber (HNLF) compared to Dispersion Shifted Fiber (DSF). The simulation results show by deployed a HNLF as a nonlinear element in Dispersion Imbalanced Loop Mirror (DILM) requires only 400mW peak powers to obtain a peak of transmission compared to DSF which requires a higher peak power at 2000mW to obtain a certain transmissivity. It also shows that HNLF required shorter fiber length to achieve the highest transmission. The 2R regenerator also increases the extinction ratio (ER) of the entire system. © 2010 IEEE.

Association of HIV-1 Envelope-Specific Breast Milk IgA Responses with Reduced Risk of Postnatal Mother-to-Child Transmission of HIV-1.

UNLABELLED: Infants born to HIV-1-infected mothers in resource-limited areas where replacement feeding is unsafe and impractical are repeatedly exposed to HIV-1 throughout breastfeeding. Despite this, the majority of infants do not contract HIV-1 postnatally, even in the absence of maternal antiretroviral therapy. This suggests that immune factors in breast milk of HIV-1-infected mothers help to limit vertical transmission. We compared the HIV-1 envelope-specific breast milk and plasma antibody responses of clade C HIV-1-infected postnatally transmitting and nontransmitting mothers in the control arm of the Malawi-based Breastfeeding Antiretrovirals and Nutrition Study using multivariable logistic regression modeling. We found no association between milk or plasma neutralization activity, antibody-dependent cell-mediated cytotoxicity, or HIV-1 envelope-specific IgG responses and postnatal transmission risk. While the envelope-specific breast milk and plasma IgA responses also did not reach significance in predicting postnatal transmission risk in the primary model after correction for multiple comparisons, subsequent exploratory analysis using two distinct assay methodologies demonstrated that the magnitudes of breast milk total and secretory IgA responses against a consensus HIV-1 envelope gp140 (B.con env03) were associated with reduced postnatal transmission risk. These results suggest a protective role for mucosal HIV-1 envelope-specific IgA responses in the context of postnatal virus transmission. This finding supports further investigations into the mechanisms by which mucosal IgA reduces risk of HIV-1 transmission via breast milk and into immune interventions aimed at enhancing this response. IMPORTANCE: Infants born to HIV-1-infected mothers are repeatedly exposed to the virus in breast milk. Remarkably, the transmission rate is low, suggesting that immune factors in the breast milk of HIV-1-infected mothers help to limit transmission. We compared the antibody responses in plasma and breast milk of HIV-1-transmitting and -nontransmitting mothers to identify responses that correlated with reduced risk of postnatal HIV-1 transmission. We found that neither plasma nor breast milk IgG antibody responses were associated with risk of HIV-1 transmission. In contrast, the magnitudes of the breast milk IgA and secretory IgA responses against HIV-1 envelope proteins were associated with reduced risk of postnatal HIV-1 transmission. The results of this study support further investigations of the mechanisms by which mucosal IgA may reduce the risk of HIV-1 transmission via breastfeeding and the development of strategies to enhance milk envelope-specific IgA responses to reduce mother-to-child HIV transmission and promote an HIV-free generation.

Impact of Genetic Testing and Family Health History Based Risk Counseling on Behavior Change and Cognitive Precursors for Type 2 Diabetes.

Family health history (FHH) in the context of risk assessment has been shown to positively impact risk perception and behavior change. The added value of genetic risk testing is less certain. The aim of this study was to determine the impact of Type 2 Diabetes (T2D) FHH and genetic risk counseling on behavior and its cognitive precursors. Subjects were non-diabetic patients randomized to counseling that included FHH +/- T2D genetic testing. Measurements included weight, BMI, fasting glucose at baseline and 12 months and behavioral and cognitive precursor (T2D risk perception and control over disease development) surveys at baseline, 3, and 12 months. 391 subjects enrolled of which 312 completed the study. Behavioral and clinical outcomes did not differ across FHH or genetic risk but cognitive precursors did. Higher FHH risk was associated with a stronger perceived T2D risk (pKendall < 0.001) and with a perception of "serious" risk (pKendall < 0.001). Genetic risk did not influence risk perception, but was correlated with an increase in perception of "serious" risk for moderate (pKendall = 0.04) and average FHH risk subjects (pKendall = 0.01), though not for the high FHH risk group. Perceived control over T2D risk was high and not affected by FHH or genetic risk. FHH appears to have a strong impact on cognitive precursors of behavior change, suggesting it could be leveraged to enhance risk counseling, particularly when lifestyle change is desirable. Genetic risk was able to alter perceptions about the seriousness of T2D risk in those with moderate and average FHH risk, suggesting that FHH could be used to selectively identify individuals who may benefit from genetic risk testing.

The association of HIV counseling and testing with HIV risk behaviors in a random population-based survey in Kisumu, Kenya.

HIV testing has been promoted as a key HIV prevention strategy in low-resource settings, despite studies showing variable impact on risk behavior. We sought to examine rates of HIV testing and the association between testing and sexual risk behaviors in Kisumu, Kenya. Participants were interviewed about HIV testing and sexual risk behaviors. They then underwent HIV serologic testing. We found that 47% of women and 36% of men reported prior testing. Two-thirds of participants who tested HIV-positive in this study reported no prior HIV test. Women who had undergone recent testing were less likely to report high-risk behaviors than women who had never been tested; this was not seen among men. Although rates of HIV testing were higher than seen in previous studies, the majority of HIV-infected people were unaware of their status. Efforts should be made to increase HIV testing among this population.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.

Predicting 10-year risk of fatal cardiovascular disease in Germany: an update based on the SCORE-Deutschland Risk Charts

Estimation of absolute risk of cardiovascular disease (CVD), preferably with population-specific risk charts, has become a cornerstone of CVD primary prevention. Regular recalibration of risk charts may be necessary due to decreasing CVD rates and CVD risk factor levels. The SCORE risk charts for fatal CVD risk assessment were first calibrated for Germany with 1998 risk factor level data and 1999 mortality statistics. We present an update of these risk charts based on the SCORE methodology including estimates of relative risks from SCORE, risk factor levels from the German Health Interview and Examination Survey for Adults 2008-11 (DEGS1) and official mortality statistics from 2012. Competing risks methods were applied and estimates were independently validated. Updated risk charts were calculated based on cholesterol, smoking, systolic blood pressure risk factor levels, sex and 5-year age-groups. The absolute 10-year risk estimates of fatal CVD were lower according to the updated risk charts compared to the first calibration for Germany. In a nationwide sample of 3062 adults aged 40-65 years free of major CVD from DEGS1, the mean 10-year risk of fatal CVD estimated by the updated charts was lower by 29% and the estimated proportion of high risk people (10-year risk > = 5%) by 50% compared to the older risk charts. This recalibration shows a need for regular updates of risk charts according to changes in mortality and risk factor levels in order to sustain the identification of people with a high CVD risk.

Selective serotonin reuptake inhibitor antidepressant use in first trimester pregnancy and risk of specific congenital anomalies: a European register-based study

Evidence of an association between early pregnancy exposure to selective serotonin reuptake inhibitors (SSRI) and congenital heart defects (CHD) has contributed to recommendations to weigh benefits and risks carefully. The objective of this study was to determine the specificity of association between first trimester exposure to SSRIs and specific CHD and other congenital anomalies (CA) associated with SSRI exposure in the literature (signals). A population-based case-malformed control study was conducted in 12 EUROCAT CA registries covering 2.1 million births 1995-2009 including livebirths, fetal deaths from 20 weeks gestation and terminations of pregnancy for fetal anomaly. Babies/fetuses with specific CHD (n = 12,876) and non-CHD signal CA (n = 13,024), were compared with malformed controls whose diagnosed CA have not been associated with SSRI in the literature (n = 17,083). SSRI exposure in first trimester pregnancy was associated with CHD overall (OR adjusted for registry 1.41, 95% CI 1.07-1.86, fluoxetine adjOR 1.43 95% CI 0.85-2.40, paroxetine adjOR 1.53, 95% CI 0.91-2.58) and with severe CHD (adjOR 1.56, 95% CI 1.02-2.39), particularly Tetralogy of Fallot (adjOR 3.16, 95% CI 1.52-6.58) and Ebstein's anomaly (adjOR 8.23, 95% CI 2.92-23.16). Significant associations with SSRI exposure were also found for ano-rectal atresia/stenosis (adjOR 2.46, 95% CI 1.06-5.68), gastroschisis (adjOR 2.42, 95% CI 1.10-5.29), renal dysplasia (adjOR 3.01, 95% CI 1.61-5.61), and clubfoot (adjOR 2.41, 95% CI 1.59-3.65). These data support a teratogenic effect of SSRIs specific to certain anomalies, but cannot exclude confounding by indication or associated factors.