Dynamic SLA management with forecasting using multi-objective optimization

Cost-efficient operation while satisfying performance and availability guarantees in Service Level Agreements (SLAs) is a challenge for Cloud Computing, as these are potentially conflicting objectives. We present a framework for SLA management based on multi-objective optimization. The framework features a forecasting model for determining the best virtual machine-to-host allocation given the need to minimize SLA violations, energy consumption and resource wasting. A comprehensive SLA management solution is proposed that uses event processing for monitoring and enables dynamic provisioning of virtual machines onto the physical infrastructure. We validated our implementation against serveral standard heuristics and were able to show that our approach is significantly better.

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

In several regions of the world, climate change is expected to have severe impacts on agricultural systems. Changes in land management are one way to adapt to future climatic conditions, including land-use changes and local adjustments of agricultural practices. In previous studies, options for adaptation have mostly been explored by testing alternative scenarios. Systematic explorations of land management possibilities using optimization approaches were so far mainly restricted to studies of land and resource management under constant climatic conditions. In this study, we bridge this gap and exploit the benefits of multi-objective regional optimization for identifying optimum land management adaptations to climate change. We design a multi-objective optimization routine that integrates a generic crop model and considers two climate scenarios for 2050 in a meso-scale catchment on the Swiss Central Plateau with already limited water resources. The results indicate that adaptation will be necessary in the study area to cope with a decrease in productivity by 0–10 %, an increase in soil loss by 25–35 %, and an increase in N-leaching by 30–45 %. Adaptation options identified here exhibit conflicts between productivity and environmental goals, but compromises are possible. Necessary management changes include (i) adjustments of crop shares, i.e. increasing the proportion of early harvested winter cereals at the expense of irrigated spring crops, (ii) widespread use of reduced tillage, (iii) allocation of irrigated areas to soils with low water-retention capacity at lower elevations, and (iv) conversion of some pre-alpine grasslands to croplands.

Applying the fuzzy analytical hierarchy process in digital marketing

The fuzzy analytical network process (FANP) is introduced as a potential multi-criteria-decision-making (MCDM) method to improve digital marketing management endeavors. Today’s information overload makes digital marketing optimization, which is needed to continuously improve one’s business, increasingly difficult. The proposed FANP framework is a method for enhancing the interaction between customers and marketers (i.e., involved stakeholders) and thus for reducing the challenges of big data. The presented implementation takes realities’ fuzziness into account to manage the constant interaction and continuous development of communication between marketers and customers on the Web. Using this FANP framework, the marketers are able to increasingly meet the varying requirements of their customers. To improve the understanding of the implementation, advanced visualization methods (e.g., wireframes) are used.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.

Evaluation of Video Transmission Energy Consumption and Quality

The widespread use of wireless enabled devices and the increasing capabilities of wireless technologies has promoted multimedia content access and sharing among users. However, the quality perceived by the users still depends on multiple factors such as video characteristics, device capabilities, and link quality. While video characteristics include the video time and spatial complexity as well as the coding complexity, one of the most important device characteristics is the battery lifetime. There is the need to assess how these aspects interact and how they impact the overall user satisfaction. This paper advances previous works by proposing and validating a flexible framework, named EViTEQ, to be applied in real testbeds to satisfy the requirements of performance assessment. EViTEQ is able to measure network interface energy consumption with high precision, while being completely technology independent and assessing the application level quality of experience. The results obtained in the testbed show the relevance of combined multi-criteria measurement approaches, leading to superior end-user satisfaction perception evaluation .

Increasing Scientific Confidence in Adverse Outcome Pathways: Application of Tailored Bradford-Hill Considerations for Evaluating Weight of Evidence.

Systematic consideration of scientific support is a critical element in developing and, ultimately, using adverse outcome pathways (AOPs) for various regulatory applications. Though weight of evidence (WoE) analysis has been proposed as a basis for assessment of the maturity and level of confidence in an AOP, methodologies and tools are still being formalized. The Organization for Economic Co-operation and Development (OECD) Users' Handbook Supplement to the Guidance Document for Developing and Assessing AOPs (OECD 2014a; hereafter referred to as the OECD AOP Handbook) provides tailored Bradford-Hill (BH) considerations for systematic assessment of confidence in a given AOP. These considerations include (1) biological plausibility and (2) empirical support (dose-response, temporality, and incidence) for Key Event Relationships (KERs), and (3) essentiality of key events (KEs). Here, we test the application of these tailored BH considerations and the guidance outlined in the OECD AOP Handbook using a number of case examples to increase experience in more transparently documenting rationales for assigned levels of confidence to KEs and KERs, and to promote consistency in evaluation within and across AOPs. The major lessons learned from experience are documented, and taken together with the case examples, should contribute to better common understanding of the nature and form of documentation required to increase confidence in the application of AOPs for specific uses. Based on the tailored BH considerations and defining questions, a prototype quantitative model for assessing the WoE of an AOP using tools of multi-criteria decision analysis (MCDA) is described. The applicability of the approach is also demonstrated using the case example aromatase inhibition leading to reproductive dysfunction in fish. Following the acquisition of additional experience in the development and assessment of AOPs, further refinement of parameterization of the model through expert elicitation is recommended. Overall, the application of quantitative WoE approaches hold promise to enhance the rigor, transparency and reproducibility for AOP WoE determinations and may play an important role in delineating areas where research would have the greatest impact on improving the overall confidence in the AOP.

Variability in the prevalence of adult ADHD in treatment seeking substance use disorder patients: results from an international multi-center study exploring DSM-IV and DSM-5 criteria

Background: Available studies vary in their estimated prevalence of attention deficit/hyperactivity disor-der (ADHD) in substance use disorder (SUD) patients, ranging from 2 to 83%. A better understanding ofthe possible reasons for this variability and the effect of the change from DSM-IV to DSM-5 is needed.Methods: A two stage international multi-center, cross-sectional study in 10 countries, among patientsform inpatient and outpatient addiction treatment centers for alcohol and/or drug use disorder patients. Atotal of 3558 treatment seeking SUD patients were screened for adult ADHD. A subsample of 1276 subjects,both screen positive and screen negative patients, participated in a structured diagnostic interview. 5AdultsResults: Prevalence of DSM-IV and DSM-5 adult ADHD varied for DSM-IV from 5.4% (CI 95%: 2.4–8.3) forHungary to 31.3% (CI 95%:25.2–37.5) for Norway and for DSM-5 from 7.6% (CI 95%: 4.1–11.1) for Hungary to32.6% (CI 95%: 26.4–38.8) for Norway. Using the same assessment procedures in all countries and centersresulted in substantial reduction of the variability in the prevalence of adult ADHD reported in previousstudies among SUD patients (2–83% → 5.4–31.3%). The remaining variability was partly explained byprimary substance of abuse and by country (Nordic versus non-Nordic countries). Prevalence estimatesfor DSM-5 were slightly higher than for DSM-IV.Conclusions: Given the generally high prevalence of adult ADHD, all treatment seeking SUD patientsshould be screened and, after a confirmed diagnosis, treated for ADHD since the literature indicates poorprognoses of SUD in treatment seeking SUD patients with ADHD.

A benchmark of kriging-based infill criteria for noisy optimization

Responses of many real-world problems can only be evaluated perturbed by noise. In order to make an efficient optimization of these problems possible, intelligent optimization strategies successfully coping with noisy evaluations are required. In this article, a comprehensive review of existing kriging-based methods for the optimization of noisy functions is provided. In summary, ten methods for choosing the sequential samples are described using a unified formalism. They are compared on analytical benchmark problems, whereby the usual assumption of homoscedastic Gaussian noise made in the underlying models is meet. Different problem configurations (noise level, maximum number of observations, initial number of observations) and setups (covariance functions, budget, initial sample size) are considered. It is found that the choices of the initial sample size and the covariance function are not critical. The choice of the method, however, can result in significant differences in the performance. In particular, the three most intuitive criteria are found as poor alternatives. Although no criterion is found consistently more efficient than the others, two specialized methods appear more robust on average.

Early non-invasive cardiac output monitoring in hemodynamically unstable intensive care patients: A multi-center randomized controlled trial

Introduction Acute hemodynamic instability increases morbidity and mortality. We investigated whether early non-invasive cardiac output monitoring enhances hemodynamic stabilization and improves outcome. Methods A multicenter, randomized controlled trial was conducted in three European university hospital intensive care units in 2006 and 2007. A total of 388 hemodynamically unstable patients identified during their first six hours in the intensive care unit (ICU) were randomized to receive either non-invasive cardiac output monitoring for 24 hrs (minimally invasive cardiac output/MICO group; n = 201) or usual care (control group; n = 187). The main outcome measure was the proportion of patients achieving hemodynamic stability within six hours of starting the study. Results The number of hemodynamic instability criteria at baseline (MICO group mean 2.0 (SD 1.0), control group 1.8 (1.0); P = .06) and severity of illness (SAPS II score; MICO group 48 (18), control group 48 (15); P = .86)) were similar. At 6 hrs, 45 patients (22%) in the MICO group and 52 patients (28%) in the control group were hemodynamically stable (mean difference 5%; 95% confidence interval of the difference -3 to 14%; P = .24). Hemodynamic support with fluids and vasoactive drugs, and pulmonary artery catheter use (MICO group: 19%, control group: 26%; P = .11) were similar in the two groups. The median length of ICU stay was 2.0 (interquartile range 1.2 to 4.6) days in the MICO group and 2.5 (1.1 to 5.0) days in the control group (P = .38). The hospital mortality was 26% in the MICO group and 21% in the control group (P = .34). Conclusions Minimally-invasive cardiac output monitoring added to usual care does not facilitate early hemodynamic stabilization in the ICU, nor does it alter the hemodynamic support or outcome. Our results emphasize the need to evaluate technologies used to measure stroke volume and cardiac output--especially their impact on the process of care--before any large-scale outcome studies are attempted.

Coupling biomechanics to a cellular level model: An approach to patient-specific image driven multi-scale and multi-physics tumor simulation

Modeling of tumor growth has been performed according to various approaches addressing different biocomplexity levels and spatiotemporal scales. Mathematical treatments range from partial differential equation based diffusion models to rule-based cellular level simulators, aiming at both improving our quantitative understanding of the underlying biological processes and, in the mid- and long term, constructing reliable multi-scale predictive platforms to support patient-individualized treatment planning and optimization. The aim of this paper is to establish a multi-scale and multi-physics approach to tumor modeling taking into account both the cellular and the macroscopic mechanical level. Therefore, an already developed biomodel of clinical tumor growth and response to treatment is self-consistently coupled with a biomechanical model. Results are presented for the free growth case of the imageable component of an initially point-like glioblastoma multiforme tumor. The composite model leads to significant tumor shape corrections that are achieved through the utilization of environmental pressure information and the application of biomechanical principles. Using the ratio of smallest to largest moment of inertia of the tumor material to quantify the effect of our coupled approach, we have found a tumor shape correction of 20\% by coupling biomechanics to the cellular simulator as compared to a cellular simulation without preferred growth directions. We conclude that the integration of the two models provides additional morphological insight into realistic tumor growth behavior. Therefore, it might be used for the development of an advanced oncosimulator focusing on tumor types for which morphology plays an important role in surgical and/or radio-therapeutic treatment planning.

On the road through the Bolivian Amazon: a multi-level land governance analysis of deforestation

Previous studies have shown that collective property rights offer higher flexibility than individual property and improve sustainable community-based forest management. Our case study, carried out in the Beni department of Bolivia, does not contradict this assertion, but shows that collective rights have been granted in areas where ecological contexts and market facilities were less favourable to intensive land use. Previous experiences suggest investigating political processes in order to understand the criteria according to which access rights were distributed. Based on remote sensing and on a multi-level land governance framework, our research confirms that land placed under collective rights, compared to individual property, is less affected by deforestation among Andean settlements. However, analysis of the historical process of land distribution in the area shows that the distribution of property rights is the result of a political process based on economic, spatial, and environmental strategies that are defined by multiple stakeholders. Collective titles were established in the more remote areas and distributed to communities with lower productive potentialities. Land rights are thus a secondary factor of forest cover change which results from diverse political compromises based on population distribution, accessibility, environmental perceptions, and expected production or extraction incomes.

An application of combinatorial optimization in the printing industry

Offset printing is a common method to produce large amounts of printed matter. We consider a real-world offset printing process that is used to imprint customer-specific designs on napkin pouches. The print- ing technology used yields a number of specific constraints. The planning problem consists of allocating designs to printing-plate slots such that the given customer demand for each design is fulfilled, all technologi- cal and organizational constraints are met and the total overproduction and setup costs are minimized. We formulate this planning problem as a mixed-binary linear program, and we develop a multi-pass matching-based savings heuristic. We report computational results for a set of problem instances devised from real-world data.

Quality control for retinal OCT in multiple sclerosis: validation of the OSCAR-IB criteria.

BACKGROUND Retinal optical coherence tomography (OCT) permits quantification of retinal layer atrophy relevant to assessment of neurodegeneration in multiple sclerosis (MS). Measurement artefacts may limit the use of OCT to MS research. OBJECTIVE An expert task force convened with the aim to provide guidance on the use of validated quality control (QC) criteria for the use of OCT in MS research and clinical trials. METHODS A prospective multi-centre (n = 13) study. Peripapillary ring scan QC rating of an OCT training set (n = 50) was followed by a test set (n = 50). Inter-rater agreement was calculated using kappa statistics. Results were discussed at a round table after the assessment had taken place. RESULTS The inter-rater QC agreement was substantial (kappa = 0.7). Disagreement was found highest for judging signal strength (kappa = 0.40). Future steps to resolve these issues were discussed. CONCLUSION Substantial agreement for QC assessment was achieved with aid of the OSCAR-IB criteria. The task force has developed a website for free online training and QC certification. The criteria may prove useful for future research and trials in MS using OCT as a secondary outcome measure in a multi-centre setting.