Scheduling medical application workloads on virtualized computing systems

This dissertation presents and evaluates a methodology for scheduling medical application workloads in virtualized computing environments. Such environments are being widely adopted by providers of "cloud computing" services. In the context of provisioning resources for medical applications, such environments allow users to deploy applications on distributed computing resources while keeping their data secure. Furthermore, higher level services that further abstract the infrastructure-related issues can be built on top of such infrastructures. For example, a medical imaging service can allow medical professionals to process their data in the cloud, easing them from the burden of having to deploy and manage these resources themselves. In this work, we focus on issues related to scheduling scientific workloads on virtualized environments. We build upon the knowledge base of traditional parallel job scheduling to address the specific case of medical applications while harnessing the benefits afforded by virtualization technology. To this end, we provide the following contributions: (1) An in-depth analysis of the execution characteristics of the target applications when run in virtualized environments. (2) A performance prediction methodology applicable to the target environment. (3) A scheduling algorithm that harnesses application knowledge and virtualization-related benefits to provide strong scheduling performance and quality of service guarantees. In the process of addressing these pertinent issues for our target user base (i.e. medical professionals and researchers), we provide insight that benefits a large community of scientific application users in industry and academia. Our execution time prediction and scheduling methodologies are implemented and evaluated on a real system running popular scientific applications. We find that we are able to predict the execution time of a number of these applications with an average error of 15%. Our scheduling methodology, which is tested with medical image processing workloads, is compared to that of two baseline scheduling solutions and we find that it outperforms them in terms of both the number of jobs processed and resource utilization by 20–30%, without violating any deadlines. We conclude that our solution is a viable approach to supporting the computational needs of medical users, even if the cloud computing paradigm is not widely adopted in its current form.

Predictors of success: Medical Laboratory Associate in Science Degree Program

Hospitals and healthcare facilities in the United States are facing serious shortages of medical laboratory personnel, which, if not addressed, stand to negatively impact patient care. The problem is compounded by a reduction in the numbers of academic programs and resulting decrease in the number of graduates to keep up with the increase in industry demands. Given these challenges, the purpose of this study was to identify predictors of success for students in a selected 2-year Medical Laboratory Technology Associate in Science Degree Program. ^ This study examined five academic factors (College Placement Test Math and Reading scores, Cumulative GPA, Science GPA, and Professional [first semester laboratory courses] GPA) and, demographic data to see if any of these factors could predict program completion. The researcher examined academic records for a 10-year period (N =158). Using a retrospective model, the correlational analysis between the variables and completion revealed a significant relationship (p < .05) for CGPA, SGPA, CPT Math, and PGPA indicating that students with higher CGPA, SGPA, CPT Math, and PGPA were more likely to complete their degree in 2 years. Binary logistic regression analysis with the same academic variables revealed PGPA was the best predictor of program completion (p < .001). ^ Additionally, the findings in this study are consistent with the academic part of the Bean and Metzner Conceptual Model of Nontraditional Student Attrition which points to academic outcome variables such as GPA as affecting attrition. Thus, the findings in this study are important to students and educators in the field of Medical Laboratory Technology since PGPA is a predictor that can be used to provide early in-program intervention to the at-risk student, thus increasing the chances of successful timely completion.^

The Other Face of Cuban Medical Diplomacy: Domestic Impact

Since 1963 Cuba has provided medical assistance to third world countries while gaining international, political and economic support from its participating liaisons. But what exactly have been Cuba’s domestic consequences of such medical diplomacy? While the Cuban government sends many of its medical professionals and supplies abroad, the country suffers from extreme scarcity and a deterioration of its healthcare system. The purpose of my research is to enquire more on the consequences of such medical diplomacy on the Cuban healthcare system and how it has affected domestic medical infrastructure, health professionals working on the island as well as the quality of service. In carrying out this examination, I will rely on the use of information from books written by Cuban medical professionals on their personal experiences within the medical system, and patients treated on the island. I will also make active use of academic journals and articles on the Cuban healthcare system. Most of what has been written on Cuban medical diplomacy and internationalist missions are favorable critiques praising the Cuban government. Nevertheless, personal testimonies found in Dr. Dessy Mendoza Rivero’s Dengue: La Epidemia Secreta de Fidel Castro and in Dr. Jose Luis Comas and Dr. Luis Ovidio Gonzales’ Cuba: Medicina y Revolucion reveal a different side to this seemingly pleasant and good natured international exchange. As a Cuban who lived on the island, I personally suffered the consequences of such medical diplomacy and believe others will find benefit in arming themselves with knowledge on the issue.