Reliability Guided Resource Allocation for Large-scale Supercomputing Systems

In high performance computing systems, parallel applications request a large number of resources for long time periods. In this scenario, if a resource fails during the application runtime, it would cause all applications using this resource to fail. The probability of application failure is tied to the inherent reliability of resources used by the application. Our investigation of high performance computing systems operating in the field has revealed a significant difference in the measured operational reliability of individual computing nodes. By adding awareness of the individual system nodes\u27 reliability to the scheduler along with the predicted reliability needs of parallel applications, reliable resources can be matched with the most demanding applications to reduce the probability of application failure arising from resource failure. In this thesis, the researcher describes a new approach developed for resource allocation that can enhance the reliability and reduce the costs of failures of large-scale parallel applications that use high performance computing systems. This approach is based on a multi-class Erlang loss system that allows us to partition system resources based on predicted resource reliability, and to size each of these partitions to bound the probability of blocking requests to each partition while simultaneously improving the reliability of the most demanding parallel applications running on the system. Using this model, the partition mean time to failure (MTTF) is maximized and the probability of blocking of resource requests directed to each partition by a scheduling system can be controlled. This new technique can be used to determine the size of the system, to service peak loads with a bounded probability of blocking to resource requests. This approach would be useful for high performance computing system operators seeking to improve the reliability, efficiency and cost-effectiveness of their systems

Analysis of Urban Heat Islands by Using Multi-Sensor and Multi-Temporal Remote Sensing Images

This doctoral dissertation research has developed models to facilitate in characterization,analysis and monitoring of urban heat islands (UHI). Over the past few years there has been evidence of mass migration of the population towards urban areas which has led to the increase in the number of mega cities (cities with more than 10 million in population) around the world. According to the UN in 2007 around 60% (from 40% in 2000) of world populations was living in urban areas. This increase in population density in and around cities has lead to several problems related to environment such as air quality, water quality, development of Urban Heat Islands (UHI), etc. The purpose of this doctoral dissertation research was to develop a synergetic merger of remote sensing with advancements in data mining techniques to address modeling and monitoring of UHI in space and in time. The effect of urban heat islands in space and over time was analyzed within this research using exploratory and quantitative models. Visualization techniques including animation were experimented with developing a mechanism to view and understand the UHI over a city. Association rule mining models were implemented to analyze the relationship between remote sensing images and geographic information system (GIS) data. This model was implemented using three different remote sensing images i.e., Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER), Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). The effect of the spatial resolution on the model and the phenomenon were analyzed in detail to determine variables which strongly associate with land use land cover (LULC) in space and in time.A non-parametric process convolution model was developed and was used to characterize UHI from MODIS time series images. The resulting characterized images were used to study the relationship between LULC and UHI. The behavior of UHI including its movement and magnitude was analyzed in space and time.The intellectual merits of these methods are two-fold; first, they will be a forerunner in the development and implementation of association rule mining algorithm within remote sensing image analysis framework. Second, since most of the existing UHI models are parametric in nature; the non-parametric approach is expected to overcome the existing problems within characterization and analysis. Parametric models pose problems (in terms of efficiency, since the implementation of such models are time consuming and need human intervention) while analyzing UHI effect from multiple imageries. These proposed models are expected to aid in effective spatial characterization and facilitate in temporal analysis and monitoring of UHI phenomenon.Umamaheshwaran, RajasekarWeng, QihaoAldrich, StephenBerta, SusanExoo, GeoffreyMausel, PaulDoctor of PhilosophyDepartment of Earth and Environmental ScienceCunningham Memorial library, Terre Haute,Indiana State University20110920-009DoctoralTitle from document title page. Document formatted into pages: contains 277p.: ill. Includes bibliography, abstract and appendi