Algorithms for scheduling parallel batch processing machines with non-identical job ready times

This research is motivated by a practical application observed at a printed circuit board (PCB) manufacturing facility. After assembly, the PCBs (or jobs) are tested in environmental stress screening (ESS) chambers (or batch processing machines) to detect early failures. Several PCBs can be simultaneously tested as long as the total size of all the PCBs in the batch does not violate the chamber capacity. PCBs from different production lines arrive dynamically to a queue in front of a set of identical ESS chambers, where they are grouped into batches for testing. Each line delivers PCBs that vary in size and require different testing (or processing) times. Once a batch is formed, its processing time is the longest processing time among the PCBs in the batch, and its ready time is given by the PCB arriving last to the batch. ESS chambers are expensive and a bottleneck. Consequently, its makespan has to be minimized. ^ A mixed-integer formulation is proposed for the problem under study and compared to a formulation recently published. The proposed formulation is better in terms of the number of decision variables, linear constraints and run time. A procedure to compute the lower bound is proposed. For sparse problems (i.e. when job ready times are dispersed widely), the lower bounds are close to optimum. ^ The problem under study is NP-hard. Consequently, five heuristics, two metaheuristics (i.e. simulated annealing (SA) and greedy randomized adaptive search procedure (GRASP)), and a decomposition approach (i.e. column generation) are proposed—especially to solve problem instances which require prohibitively long run times when a commercial solver is used. Extensive experimental study was conducted to evaluate the different solution approaches based on the solution quality and run time. ^ The decomposition approach improved the lower bounds (or linear relaxation solution) of the mixed-integer formulation. At least one of the proposed heuristic outperforms the Modified Delay heuristic from the literature. For sparse problems, almost all the heuristics report a solution close to optimum. GRASP outperforms SA at a higher computational cost. The proposed approaches are viable to implement as the run time is very short. ^

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.

Does core strength training influence kinetic efficiency, lower extremity stability, and 5000m performance in runners?

Context: Core strength training (CST) has been popular in the fitness industry for a decade. Although strong core muscles are believed to enhance athletic performance, only few scientific studies have been conducted to identify the effectiveness of CST on improving athletic performance. Objective: Identify the effects of a 6-wk CST on running kinetics, lower extremity stability, and running performance in recreational and competitive runners. Design and Setting: A test-retest, randomized control design was used to assess the effect of CST and no CST on ground reaction force (GRF), lower extremity stability scores, and running performance. Participants: Twenty-eight healthy adults (age, 36.9+9.4yrs, height, 168.4+9.6cm, mass, 70.1+15.3kg) were recruited and randomly divided into two groups. Main outcome Measures: GRF was determined by calculating peak impact vertical GRF (vGRF), peak active vGRF, duration of the breaking or horizontal GRF (hGRF), and duration of the propulsive hGRF as measured while running across a force plate. Lower extremity stability in three directions (anterior, posterior, lateral) was assessed using the Star Excursion Balance Test (SEBT). Running performance was determined by 5000 meter run measured on selected outdoor tracks. Six 2 (time) X 2 (condition) mixed-design ANOVA were used to determine if CST influences on each dependent variable, p < .05. Results: No significant interactions were found for any kinetic variables and SEBT score, p>.05. But 5000m run time showed significant interaction, p < .05. SEBT scores improved in both groups, but more in the experimental group. Conclusion: CST did not significantly influence kinetic efficiency and lower extremity stability, but did influence running performance.

Assessment of the occurrence and potential risks of antibiotics and their metabolites in South Florida waters using liquid chromatography tandem mass spectrometry

An automated on-line SPE-LC-MS/MS method was developed for the quantitation of multiple classes of antibiotics in environmental waters. High sensitivity in the low ng/L range was accomplished by using large volume injections with 10-mL of sample. Positive confirmation of analytes was achieved using two selected reaction monitoring (SRM) transitions per antibiotic and quantitation was performed using an internal standard approach. Samples were extracted using online solid phase extraction, then using column switching technique; extracted samples were immediately passed through liquid chromatography and analyzed by tandem mass spectrometry. The total run time per each sample was 20 min. The statistically calculated method detection limits for various environmental samples were between 1.2 and 63 ng/L. Furthermore, the method was validated in terms of precision, accuracy and linearity. ^ The developed analytical methodology was used to measure the occurrence of antibiotics in reclaimed waters (n=56), surface waters (n=53), ground waters (n=8) and drinking waters (n=54) collected from different parts of South Florida. In reclaimed waters, the most frequently detected antibiotics were nalidixic acid, erythromycin, clarithromycin, azithromycin trimethoprim, sulfamethoxazole and ofloxacin (19.3-604.9 ng/L). Detection of antibiotics in reclaimed waters indicates that they can't be completely removed by conventional wastewater treatment process. Furthermore, the average mass loads of antibiotics released into the local environment through reclaimed water were estimated as 0.248 Kg/day. Among the surface waters samples, Miami River (reaching up to 580 ng/L) and Black Creek canal (up to 124 ng/L) showed highest concentrations of antibiotics. No traces of antibiotics were found in ground waters. On the other hand, erythromycin (monitored as anhydro erythromycin) was detected in 82% of the drinking water samples (n.d-66 ng/L). The developed approach is suitable for both research and monitoring applications.^ Major metabolites of antibiotics in reclaimed wates were identified and quantified using high resolution benchtop Q-Exactive orbitrap mass spectrometer. A phase I metabolite of erythromycin was tentatively identified in full scan based on accurate mass measurement. Using extracted ion chromatogram (XIC), high resolution data-dependent MS/MS spectra and metabolic profiling software the metabolite was identified as desmethyl anhydro erythromycin with molecular formula C36H63NO12 and m/z 702.4423. The molar concentration of the metabolite to erythromycin was in the order of 13 %. To my knowledge, this is the first known report on this metabolite in reclaimed water. Another compound acetyl-sulfamethoxazole, a phase II metabolite of sulfamethoxazole was also identified in reclaimed water and mole fraction of the metabolite represent 36 %, of the cumulative sulfamethoxazole concentration. The results were illustrating the importance to include metabolites also in the routine analysis to obtain a mass balance for better understanding of the occurrence, fate and distribution of antibiotics in the environment. ^ Finally, all the antibiotics detected in reclaimed and surface waters were investigated to assess the potential risk to the aquatic organisms. The surface water antibiotic concentrations that represented the real time exposure conditions revealed that the macrolide antibiotics, erythromycin, clarithromycin and tylosin along with quinolone antibiotic, ciprofloxacin were suspected to induce high toxicity to aquatic biota. Preliminary results showing that, among the antibiotic groups tested, macrolides posed the highest ecological threat, and therefore, they may need to be further evaluated with, long-term exposure studies considering bioaccumulation factors and more number of species selected. Overall, the occurrence of antibiotics in aquatic environment is posing an ecological health concern.^

A simulation-based decision support system for evaluating operating policies in an emergency room facility

Increased pressure to control costs and increased competition has prompted health care managers to look for tools to effectively operate their institutions. This research sought a framework for the development of a Simulation-Based Decision Support System (SB-DSS) to evaluate operating policies. A prototype of this SB-DSS was developed. It incorporates a simulation model that uses real or simulated data. ER decisions have been categorized and, for each one, an implementation plan has been devised. Several issues of integrating heterogeneous tools have been addressed. The prototype revealed that simulation can truly be used in this environment in a timely fashion because the simulation model has been complemented with a series of decision-making routines. These routines use a hierarchical approach to organize the various scenarios under which the model may run and to partially reconfigure the ARENA model at run time. Hence, the SB-DSS tailors its responses to each node in the hierarchy.

Assessment of the Occurrence and Potential Risks of Antibiotics and their Metabolites in South Florida Waters Using Liquid Chromatography Tandem Mass Spectrometry

An automated on-line SPE-LC-MS/MS method was developed for the quantitation of multiple classes of antibiotics in environmental waters. High sensitivity in the low ng/L range was accomplished by using large volume injections with 10-mL of sample. Positive confirmation of analytes was achieved using two selected reaction monitoring (SRM) transitions per antibiotic and quantitation was performed using an internal standard approach. Samples were extracted using online solid phase extraction, then using column switching technique; extracted samples were immediately passed through liquid chromatography and analyzed by tandem mass spectrometry. The total run time per each sample was 20 min. The statistically calculated method detection limits for various environmental samples were between 1.2 and 63 ng/L. Furthermore, the method was validated in terms of precision, accuracy and linearity. The developed analytical methodology was used to measure the occurrence of antibiotics in reclaimed waters (n=56), surface waters (n=53), ground waters (n=8) and drinking waters (n=54) collected from different parts of South Florida. In reclaimed waters, the most frequently detected antibiotics were nalidixic acid, erythromycin, clarithromycin, azithromycin trimethoprim, sulfamethoxazole and ofloxacin (19.3-604.9 ng/L). Detection of antibiotics in reclaimed waters indicates that they can’t be completely removed by conventional wastewater treatment process. Furthermore, the average mass loads of antibiotics released into the local environment through reclaimed water were estimated as 0.248 Kg/day. Among the surface waters samples, Miami River (reaching up to 580 ng/L) and Black Creek canal (up to 124 ng/L) showed highest concentrations of antibiotics. No traces of antibiotics were found in ground waters. On the other hand, erythromycin (monitored as anhydro erythromycin) was detected in 82% of the drinking water samples (n.d-66 ng/L). The developed approach is suitable for both research and monitoring applications. Major metabolites of antibiotics in reclaimed wates were identified and quantified using high resolution benchtop Q-Exactive orbitrap mass spectrometer. A phase I metabolite of erythromycin was tentatively identified in full scan based on accurate mass measurement. Using extracted ion chromatogram (XIC), high resolution data-dependent MS/MS spectra and metabolic profiling software the metabolite was identified as desmethyl anhydro erythromycin with molecular formula C36H63NO12 and m/z 702.4423. The molar concentration of the metabolite to erythromycin was in the order of 13 %. To my knowledge, this is the first known report on this metabolite in reclaimed water. Another compound acetyl-sulfamethoxazole, a phase II metabolite of sulfamethoxazole was also identified in reclaimed water and mole fraction of the metabolite represent 36 %, of the cumulative sulfamethoxazole concentration. The results were illustrating the importance to include metabolites also in the routine analysis to obtain a mass balance for better understanding of the occurrence, fate and distribution of antibiotics in the environment. Finally, all the antibiotics detected in reclaimed and surface waters were investigated to assess the potential risk to the aquatic organisms. The surface water antibiotic concentrations that represented the real time exposure conditions revealed that the macrolide antibiotics, erythromycin, clarithromycin and tylosin along with quinolone antibiotic, ciprofloxacin were suspected to induce high toxicity to aquatic biota. Preliminary results showing that, among the antibiotic groups tested, macrolides posed the highest ecological threat, and therefore, they may need to be further evaluated with, long-term exposure studies considering bioaccumulation factors and more number of species selected. Overall, the occurrence of antibiotics in aquatic environment is posing an ecological health concern.

Meshfree Method for Prediction of Thermal Properties of Porous Ceramic Materials

In the presented thesis work, meshfree method with distance fields is applied to create a novel computational approach which enables inclusion of the realistic geometric models of the microstructure and liberates Finite Element Analysis(FEA) from thedependance on and limitations of meshing of fine microstructural feature such as splats and porosity.Manufacturing processes of ceramics produce materials with complex porosity microstructure.Geometry of pores, their size and location substantially affect macro scale physical properties of the material. Complex structure and geometry of the pores severely limit application of modern Finite Element Analysis methods because they require construction of spatial grids (meshes) that conform to the geometric shape of the structure. As a result, there are virtually no effective tools available for predicting overall mechanical and thermal properties of porous materials based on their microstructure. This thesis is a separate handling and controls of geometric and physical computational models that are seamlessly combined at solution run time. Using the proposedapproach we will determine the effective thermal conductivity tensor of real porous ceramic materials featuring both isotropic and anisotropic thermal properties. This work involved development and implementation of numerical algorithms, data structure, and software.

Algorithms for efficient transaction management and consistent queries in client -server semantic object-oriented parallel databases

Large read-only or read-write transactions with a large read set and a small write set constitute an important class of transactions used in such applications as data mining, data warehousing, statistical applications, and report generators. Such transactions are best supported with optimistic concurrency, because locking of large amounts of data for extended periods of time is not an acceptable solution. The abort rate in regular optimistic concurrency algorithms increases exponentially with the size of the transaction. The algorithm proposed in this dissertation solves this problem by using a new transaction scheduling technique that allows a large transaction to commit safely with significantly greater probability that can exceed several orders of magnitude versus regular optimistic concurrency algorithms. A performance simulation study and a formal proof of serializability and external consistency of the proposed algorithm are also presented.^ This dissertation also proposes a new query optimization technique (lazy queries). Lazy Queries is an adaptive query execution scheme which optimizes itself as the query runs. Lazy queries can be used to find an intersection of sub-queries in a very efficient way, which does not require full execution of large sub-queries nor does it require any statistical knowledge about the data.^ An efficient optimistic concurrency control algorithm used in a massively parallel B-tree with variable-length keys is introduced. B-trees with variable-length keys can be effectively used in a variety of database types. In particular, we show how such a B-tree was used in our implementation of a semantic object-oriented DBMS. The concurrency control algorithm uses semantically safe optimistic virtual "locks" that achieve very fine granularity in conflict detection. This algorithm ensures serializability and external consistency by using logical clocks and backward validation of transactional queries. A formal proof of correctness of the proposed algorithm is also presented. ^

Optimization of adaptive traffic signal control with transit signal priority at isolated intersections using parallel genetic algorithms

Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This dissertation presents a new method that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of transit signal priority (TSP). The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. Unlike the simple genetic algorithm (GA), PGA can provide better and faster solutions needed for real-time optimization of adaptive traffic signal control. ^ An important component in the proposed method involves the development of a microscopic delay estimation model that was designed specifically to optimize adaptive traffic signal with TSP. Macroscopic delay models such as the Highway Capacity Manual (HCM) delay model are unable to accurately consider the effect of phase combination and phase sequence in delay calculations. In addition, because the number of phases and the phase sequence of adaptive traffic signal may vary from cycle to cycle, the phase splits cannot be optimized when the phase sequence is also a decision variable. A "flex-phase" concept was introduced in the proposed microscopic delay estimation model to overcome these limitations. ^ The performance of PGA was first evaluated against the simple GA. The results show that PGA achieved both faster convergence and lower delay for both under- or over-saturated traffic conditions. A VISSIM simulation testbed was then developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP. The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer was able to produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles. The VISSIM testbed developed in this research provides a powerful tool to design and evaluate different TSP strategies under both actuated and adaptive signal control. ^

Investigating the origin of PM2.5 in Baltimore using highly time-resolved organic molecular markers measured during the Baltimore PM-supersite

The detailed organic composition of atmospheric fine particles with an aerodynamic diameter smaller than or equal to 2.5 micrometers (PM2.5) is an integral part of the knowledge needed in order to fully characterize its sources and transformation in the environment. For the study presented here, samples were collected at 3-hour intervals. This high time resolution allows gaining unique insights on the influence of short- and long-range transport phenomena, and dynamic atmospheric processes. A specially designed sequential sampler was deployed at the 2002-2003 Baltimore PM-Supersite to collect PM2.5 samples at a 3-hourly resolution for extended periods of consecutive days, during both summer and winter seasons. Established solvent-extraction and GC-MS techniques were used to extract and analyze the organic compounds in 119 samples from each season. Over 100 individual compounds were quantified in each sample. For primary organics, averaging the diurnal ambient concentrations over the sampled periods revealed ambient patterns that relate to diurnal emission patterns of major source classes. Several short-term releases of pollutants from local sources were detected, and local meteorological data was used to pinpoint possible source regions. Biogenic secondary organic compounds were detected as well, and possible mechanisms of formation were evaluated. The relationships between the observed continuous variations of the concentrations of selected organic markers and both the on-site meteorological measurements conducted parallel to the PM2.5 sampling, and the synoptic patterns of weather and wind conditions were also examined. Several one-to-two days episodes were identified from the sequential variation of the concentration observed for specific marker compounds and markers ratios. The influence of the meteorological events on the concentrations of the organic compounds during selected episodes was discussed. It was observed that during the summer, under conditions of pervasive influence of air masses originated from the west/northwest, some organic species displayed characteristics consistent with the measured PM2.5 being strongly influenced by the aged nature of these long-traveling background parcels. During the winter, intrusions from more regional air masses originating from the south and the southwest were more important.

Analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing

Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

Low-income first time mothers: Effects on advanced practice nurse (APN) follow up telephone calls on maternal and infant health and health care charges

The United States has over 4 million births annually. Currently healthy women with non-complicated deliveries receive little to no routine postpartum support when discharged from the hospital. This is especially problematic if mothers are first time mothers, poor, have language barriers and little to no social support after giving birth. The purpose of this randomized clinical trial was to compare maternal and infant health outcomes, and health care charges between 2 groups of mothers and newborns. A control ( n = 69) group received routine posthospital discharge care. An intervention group (n = 70) received routine posthospital discharge care plus follow up telephone calls by advanced practice nurses (APNs) on days 3,7,14,21,28 and week 8. Both groups were followed for the first 8 weeks posthospital discharge following delivery to examine maternal health outcomes (perceived maternal stress, social support and perceived maternal physical health), infant health outcomes (routine medical follow up visits immunizations, weight gain), morbidity (urgent care visits, emergency room visits, rehospitalizations), health care charges (urgent care visits, emergency room visits, rehospitalizations) in both groups and charges for APN follow up in the intervention group only. Data were analyzed using descriptive statistics and two-sample t-tests. Study findings indicated that intervention group had significantly lower perceived maternal stress, significantly higher rating of perceived maternal health and higher levels of social support and by the end of the 2nd month posthospital discharge compared to control group mothers. Infants in the intervention group had: increased number of immunizations; fewer emergency room visits; and 1 infant rehospitalization compared to 3 infant rehospitalizations in the control group. The intervention groups' health care charges were significantly lower compared to the control group $14,333/$497 vs. $70,834/$1,068. These study results indicate that an intervention of APN follow up telephone calls in this sample of first time low-income culturally diverse mothers was an effective, safe, low cost, easy to apply intervention which improved mothers' and infants' health outcomes and reduced healthcare charges.

Voting with their feet: Migration, partisanship, and party-safe elections in Florida

Political scientists have long noted that Congressional elections are often uncompetitive, often extremely so. Many scholars argue that the cause lies in the partisan redistricting of Congressional districts, or “gerrymandering”. Other scholars emphasize polarization created by a fragmented news media, or the candidate choices made by a more ideological primary electorate. All these explanations identify the cause of party-safe elections in institutions of various kinds. This dissertation, by contrast, presents a structural explanation of uncompetitive elections. My theory is that population composition and patterns of migration are significant causes and predictors of election results in Florida. I test this theory empirically by comparing the predictions from four hypotheses against aggregate data, using the county as the unit of analysis. The first hypothesis is that Florida can be divided into clearly distinguishable, persistent partisan sections. This hypothesis is confirmed. The second hypothesis is that Florida voters have become increasingly partisan over time. This hypothesis is confirmed. The third hypothesis is that the degree of migration into a county predicts how that county will vote. This hypothesis is partially confirmed, for the migration effect appears to have waned over time. The last hypothesis is that the degree of religiosity of a county population is a predictor of how that county will vote. This hypothesis is also supported by the results of statistical analysis. By identifying the structural causes of party-safe elections, this dissertation not only broadens our understanding of elections in Florida, but also sheds light on the current polarization in American politics.

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.

A large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling

With the exponential increasing demands and uses of GIS data visualization system, such as urban planning, environment and climate change monitoring, weather simulation, hydrographic gauge and so forth, the geospatial vector and raster data visualization research, application and technology has become prevalent. However, we observe that current web GIS techniques are merely suitable for static vector and raster data where no dynamic overlaying layers. While it is desirable to enable visual explorations of large-scale dynamic vector and raster geospatial data in a web environment, improving the performance between backend datasets and the vector and raster applications remains a challenging technical issue. This dissertation is to implement these challenging and unimplemented areas: how to provide a large-scale dynamic vector and raster data visualization service with dynamic overlaying layers accessible from various client devices through a standard web browser, and how to make the large-scale dynamic vector and raster data visualization service as rapid as the static one. To accomplish these, a large-scale dynamic vector and raster data visualization geographic information system based on parallel map tiling and a comprehensive performance improvement solution are proposed, designed and implemented. They include: the quadtree-based indexing and parallel map tiling, the Legend String, the vector data visualization with dynamic layers overlaying, the vector data time series visualization, the algorithm of vector data rendering, the algorithm of raster data re-projection, the algorithm for elimination of superfluous level of detail, the algorithm for vector data gridding and re-grouping and the cluster servers side vector and raster data caching.