Discovering Job Preemptions in the Open Science Grid

The Open Science Grid(OSG) is a world-wide computing system which facilitates distributed computing for scientific research. It can distribute a computationally intensive job to geo-distributed clusters and process job's tasks in parallel. For compute clusters on the OSG, physical resources may be shared between OSG and cluster's local user-submitted jobs, with local jobs preempting OSG-based ones. As a result, job preemptions occur frequently in OSG, sometimes significantly delaying job completion time. We have collected job data from OSG over a period of more than 80 days. We present an analysis of the data, characterizing the preemption patterns and different types of jobs. Based on observations, we have grouped OSG jobs into 5 categories and analyze the runtime statistics for each category. we further choose different statistical distributions to estimate probability density function of job runtime for different classes.Comment: 8 page

New insights on the impact of coefficient instability on ratio-correlation population estimates

In this study we examine the regression-based ratio-correlation method and suggest some new tools for assessing the magnitude and impact of coefficient instability on population estimation errors. We use a robust sample of 904 counties from 11 states and find that: (1) coefficient instability is not a universal source of error in regression models for population estimation and its impact is less than commonly assumed; (2) coefficient instability is not related to bias, but it does decrease precision and increase the allocation error of population estimates; and (3) unstable coefficients have the greatest impact on counties under 20,000 in population size. Our findings suggest that information about the conditions that affect coefficient instability and its impact on estimation error might lead to more targeted and efficient approaches for improving population estimates developed from regression models

The Impact of Digitization on Product Offerings: Using Direct Digital Manufacturing in the Supply Chain

To remain competitive, supply chain managers must constantly improve their processes and react to ever-growing and evolving customer preferences in a changing business environment. However, many companies have reached diminishing returns for many of their business processes. Digitization has begun to change product offerings and these changes could be the next great source of competitive advantage for supply chain managers. This research uses a demand supply integration framework to examine direct digital manufacturing (DDM) applications being used to change product delivery to consumers. To test hypotheses, press announcements were collected and analyzed with content analysis. We find that, of the implementations of DDM for delivering products to consumers using digitization, 61% are demand-side focused innovations, 39% are supply-side focused innovations, and 9% are both demand-supply integrated innovations

Charting the Course for Energy Efficiency in New York: Lessons from Existing Programs

This report examines the performance of the existing suite of energy efficiency efforts run by the New York State Energy Research and Development Authority and the state’s investor owned utilities. The latest data shows that through 2014 EEPS program administrators had achieved 79 percent of their to-date savings goals. The report focuses on the best ways to transition from the EEPS program model to the emerging REV model. Reviewing publicly available information, this analysis takes stock of what the EEPS has achieved and calls for a REV planning and delivery program that builds upon lessons learned from decades of past efforts to achieve self-sustaining efficiency markets. It 1) describes the proposed changes to energy efficiency delivery currently under consideration by the Cuomo Administration, 2) reviews overall EEPS performance through the third quarter of 2014, 3) recommends a framework to serve as the basis for future decision-making, and 4) makes additional recommendations for the future of energy efficiency efforts in New York State

Using cohort change ratios to estimate life expectancy in populations with negligible migration: A new approach

Census survival methods are the oldest and most widely applicable methods of estimating adult mortality, and for populations with negligible migration they can provide excellent results. The reason for this ubiquity is threefold: (1) their data requirements are minimal in that only two successive age distributions are needed; (2) the two successive age distributions are usually easily obtained from census counts; and (3) the method is straightforward in that it requires neither a great deal of judgment nor “data-fitting” techniques to implement. This ubiquity is in contrast to other methods, which require more data, as well as judgment and, often, data fitting. In this short note, the new approach we demonstrate is that life expectancy at birth can be computed by using census survival rates in combination with an identity whereby the radix of a life table is equal to 1 (l0 = 1.00). We point out that our suggested method is less involved than the existing approach. We compare estimates using our approach against other estimates, and find it works reasonably well. As well as some nuances and cautions, we discuss the benefits of using this approach to estimate life expectancy, including the ability to develop estimates of average remaining life at any age. We believe that the technique is worthy of consideration for use in estimating life expectancy in populations that experience negligible migration

Hypothesis Testing in GWAS and Statistical Issues with Compensation in Clinical Trials

We first show theoretically and in simulation how power varies as a function of SNP correlation structure with currently-implemented gene-based testing methods. We propose alternative testing methods whose power does not vary with the correlation structure. We then propose hypothesis tests for detecting prevalence-incidence bias in case-control studies, a bias perhaps overrepresented in GWAS due to currently used study designs. Lastly, we hypothesize how different incentive structures used to keep clinical trial participants in studies may interact with a background of dependent censoring and result in variation in the bias of the Kaplan-Meier survival curve estimator