The parallel nature of process execution on chip multiprocessors (CMPs) has considerably boosted levels of application performance in the past decade. Generally, a certain number of computing resources are shared among the several cores of a CMP, such as shared last-level caches, shared-buses, and shared-memory. This ensures architectural simplicity while also boosting performance for multi-threaded applications. However, a consequence of sharing computing resources is that concurrently executing applications may suffer performance degradation if their collective resource requirements exceed the total amount of resources available. If resource allocation is not carefully considered, the potential performance gain from having multiple cores may be outweighed by the losses due to contention among processes for shared resources. Furthermore, CMPs with inbuilt dynamic voltage-frequency scaling (DVFS) may try to compensate for the performance loss by scaling to a higher frequency. For performance degradation due to shared-resource contention, this does not necessarily improve performance but guarantees a significant penalty on power consumption due to the quadratic relation of electrical power and voltage (P ∝ V^{2}*f).

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The area of focus for this research is the Stochastic Resource Constrained Project Scheduling Problem (SRCPSP) with Stochastic Task Insertion (STI). The STI problem is a specific form of the SRCPSP, which may be considered to be a cross between two types of problems in the general form: the Stochastic Project Scheduling Problem, and the Resource Constrained Project Scheduling Problem. The stochastic nature of this problem is in the occurrence/non-occurrence of tasks with deterministic duration. Researchers Selim (2002) and Grey (2007) laid the groundwork for the research on this problem. Selim (2002) developed a set of robustness metrics and used these to evaluate two initial baseline (predictive) scheduling techniques, optimistic (0% buffer) and pessimistic (100% buffer), where none or all of the stochastic tasks were scheduled, respectively. Grey (2007) expanded the research by developing a new partial buffering strategy for the initial baseline predictive schedule for this problem and found the partial buffering strategy to be superior to Selim s extreme buffering approach. The current research continues this work by focusing on resource aspects of the problem, new buffering approaches, and a new rescheduling method. If resource usage is important to project managers, then a set of metrics that describes changes to the resource flow would be important to measure between the initial baseline predictive schedule and the final as-run schedule. Two new sets of resource metrics were constructed regarding resource utilization and resource flow. Using these new metrics, as well as the Selim/Grey metrics, a new buffering approach was developed that used resource information to size the buffers. The resource-sized buffers did not show to have significant improvement over Grey s 50% buffer used as a benchmark. The new resource metrics were used to validate that the 50% buffering strategy is superior to the 0% or 100% buffering by Selim. Recognizing that partial buffers appear to be the most promising initial baseline development approach for STI problems, and understanding that experienced project managers may be able to predict stochastic probabilities based on prior projects, the next phase of the research developed a new set of buffering strategies where buffers are inserted that are proportional to the probability of occurrence. The results of this proportional buffering strategy were very positive, with the majority of the metrics (both robustness and resource), except for stability metrics, improved by using the proportional buffer. Finally, it was recognized that all research thus far for the SRCPSP with STI focused solely on the development of predictive schedules. Therefore, the final phase of this research developed a new reactive strategy that tested three different rescheduling points during schedule eventuation when a complete rescheduling of the latter portion of the schedule would occur. The results of this new reactive technique indicate that rescheduling improves the schedule performance in only a few metrics under very specific network characteristics (those networks with the least restrictive parameters). This research was conducted with extensive use of Base SAS v9.2 combined with SAS/OR procedures to solve project networks, solve resource flow problems, and implement reactive scheduling heuristics. Additionally, Base SAS code was paired with Visual Basic for Applications in Excel 2003 to implement an automated Gantt chart generator that provided visual inspection for validation of the repair heuristics. The results of this research when combined with the results of Selim and Grey provide strong guidance for project managers regarding how to develop baseline predictive schedules and how to reschedule the project as stochastic tasks (e.g. unplanned work) do or do not occur. Specifically, the results and recommendations are provided in a summary tabular format that describes the recommended initial baseline development approach if a project manager has a good idea of the level and location of the stochasticity for the network, highlights two cases where rescheduling during schedule eventuation may be beneficial, and shows when buffering proportional to the probability of occurrence is recommended, or not recommended, or the cases where the evidence is inconclusive.

This dissertation first presents an uniprocessor real-time scheduling algorithm called the Generic Benefit Scheduling algorithm (or GBS). GBS solves a previously open real-time scheduling problem: scheduling activities subject to arbitrarily shaped, time/utility function (TUF) time constraints and mutual exclusion resource constraints. A TUF specifies the utility of completing an application activity as an application- or situation-specific function of when that activity completes. GBS considers the scheduling objective of maximizing system-wide, total accrued utility, while respecting mutual exclusion constraints. Since this problem is NP-hard, GBS heuristically computes schedules in polynomial-time. The performance of the GBS algorithm is evaluated through simulation and through an implementation on a Portable Operating System Interface (POSIX)-compliant real-time operating system. The simulation studies and implementation measurements reveal that GBS performs close to, if not better than existing algorithms for the cases that they apply. Further, the results verify the effectiveness of GBS for its unique model. We also analytically establish timeliness and non-timeliness properties of GBS including bounds on activity utilities and mutual exclusion. GBS targets real-time systems that are subject to significant non-determinism inherent in their operating environments e.g., completely unknown activity arrivals. When system uncertainties can be stochastically characterized (e.g., stochastic activity arrivals and execution times), it is possible to provide stochastic assurances on timeliness behavior. The dissertation also presents algorithmic solutions to fundamental assurance problems in TUF-driven real-time systems, including stochastically satisfying individual, activity utility lower bounds and system-wide, total utility lower bounds. The algorithmic solutions include algorithms for processor bandwidth allocation and TUF scheduling. While bandwidth allocation algorithms allocate processor bandwidth share to activities to satisfy utility lower bounds, TUF scheduling algorithms schedule activities to maximize accrued utility. The algorithmic solutions and analysis are extended with a class of lock-free and lock-based resource access protocols to satisfy mutual exclusion constraints. We show that satisfying utility lower bounds with lock-based resource access protocols does not imply doing so with the lock-free scheme, and vice versa. Finally, the dissertation presents a rule-based framework for trading off assurance requirements on utility lower bound satisfaction. / Ph. D.

Multi-criteria scheduling provides the opportunity to create mathematical optimization models that are applicable to a diverse set of problem domains in the business world. This research addresses two different employee scheduling applications using multi-criteria objectives that present decision makers with trade-offs between global optimality and the level of disruption to current operating resources. Additionally, it investigates a scheduling problem from the product testing domain and proposes a heuristic solution technique for the problem that is shown to produce very high-quality solutions in short amounts of time. Chapter 2 addresses a grant administration workload-to-staff assignment problem that occurs in the Office of Research and Sponsored Programs at land-grant universities. We identify the optimal workload assignment plan which differs considerably due to multiple reassignments from the current state. To achieve the optimal workload reassignment plan we demonstrate a technique to identify the n best reassignments from the current state that provides the greatest progress toward the utopian solution. Solving this problem over several values of n and plotting the results allows the decision maker to visualize the reassignments and the progress achieved toward the utopian balanced workload solution. Chapter 3 identifies a weekly schedule that seeks the most cost-effective set of coach-to-program assignments in a gymnastics facility. We identify the optimal assignment plan using an integer linear programming model. The optimal assignment plan differs greatly from the status quo; therefore, we utilize a similar approach from Chapter 2 and use a multiple objective optimization technique to identify the n best staff reassignments. Again, the decision maker can visualize the trade-off between the number of reassignments and the resulting progress toward the utopian staffing cost solution and make an informed decision about the best number of reassignments. Chapter 4 focuses on product test scheduling in the presence of in-process and at-completion inspection constraints. Such testing arises in the context of the manufacture of products that must perform reliably in extreme environmental conditions. Each product receives a certification at the successful completion of a predetermined series of tests. Operational efficiency is enhanced by determining the optimal order and start times of tests so as to minimize the make span while ensuring that technicians are available when needed to complete in-process and at-completion inspections We first formulate a mixed-integer programming model (MILP) to identify the optimal solution to this problem using IBM ILOG CPLEX Interactive Optimizer 12.7. We also present a genetic algorithm (GA) solution that is implemented and solved in Microsoft Excel. Computational results are presented demonstrating the relative merits of the MILP and GA solution approaches across a number of scenarios. / Ph. D.

Recently, technological developments have affected most lifestyles, especially with the growth in Internet usage. Internet applications highlight the E-commerce capabilities and applications which are now available everywhere; they receive a great number of users on a 24-7 basis because online services are easy to use, faster and cheaper to acquire. Thus E-commerce web sites have become crucial for companies to increase their revenues. This importance has identified certain effective requirements needed from the performance of these applications. In particular, if the web server is overloaded, poor performance can result, due to either a huge rate of requests being generated which are beyond the server's capacity, or due to saturation of the communication links capacity which connects the web server to the network. Recent researches consider the overload issue and explore different mechanisms for managing the performance of E-commerce applications under overload condition. This thesis proposes a formal approach in order to investigate the effects of the extreme load and the number of dropped requests on the performance of E- III commerce web servers. The proposed approach is based on the class-based priority scheme that classifies E-commerce requests into different classes. Because no single technique can solve all aspects of overload problems, this research combines several techniques including: admission control mechanism, session-based admission control, service differentiation, request scheduling and queuing model-based approach. Request classification is based on the premise that some requests (e.g. buy) are generally considered more important than others (e.g. browse or search). Moreover, this research considers the extended models from Priority Scheduling Mechanism (PSM). These models add a new parameter, such as a review model or modify the basic PSM to low priority fair model, after the discovery of ineffectiveness with low priority customers or to add new features such as portal models. The proposed model is formally specified using the π-calculus in early stage of models design and a multi-actor simulation was developed to reflect the target models as accurately as possible and is implemented as a Java-based prototype system. A formal specification that captures the essential PSM features while keeping the performance model sufficiently simple is presented. Furthermore, the simplicity of the UML bridges the gap between π-calculus and Java programming language. IV There are many metrics for measuring the performance of E-commerce web servers. This research focuses on the performance of E-commerce web servers that refer to the throughput, utilisation, average response time, dropped requests and arrival rate. A number of experiments are conducted in order to test the performance management of the proposed approaches.

Nowadays, computer systems are everywhere. From simple portable devices such as watches and MP3 players to large stationary installations that control nuclear power plants, computer systems are now present in all aspects of our modern and every-day life. In about only 70 years, they have completely perturbed our way of life and they reached a so high degree of sophistication that they will be soon capable of driving our cars and cleaning our houses without any human intervention. As computer systems gain in responsibilities, it becomes essential that they provide both safety and reliability. Indeed, a failure in systems such as the anti-lock braking system (ABS) in cars could threaten human lives and generate catastrophic and irreversible consequences. Hence, for many years, researchers have addressed these emerging problems of system safety and reliability which come along with this fulgurant evolution. This thesis provides a general overview of embedded real-time computer systems, i.e., a particular kind of computer system whose number grows daily. We provide the reader with some preliminary knowledge and a good understanding of the concepts that underlie this emerging technology. We focus especially on the theoretical problems related to the real-time issue and briefly summarizes the main solutions, together with their advantages and drawbacks. This brings the reader through all the conceptual layers constituting a computer system, from the software level---the logical part---that specifies both the system behavior and requirements to the hardware level---the physical part---that actually performs the expected treatments and reacts to the environment. In the meanwhile, we introduce the theoretical models that allow researchers for theoretical analyses which ensure that all the system requirements are fulfilled. Finally, we address the energy consumption problem in embedded systems. We describe the various factors of power dissipation in modern technologies and we introduce different solutions to reduce this consumption./Cette thèse se focalise sur un type de systèmes informatiques bien précis appelés “systèmes embarqués temps réel”. Un système est dit “embarqué” lorsqu’il est développé afin de servir un but bien précis. Un téléphone portable est un parfait exemple de système embarqué étant donné que toutes ses fonctionnalités sont rigoureusement définies avant même sa conception. Au contraire, un ordinateur personnel n’est généralement pas considéré comme un système embarqué, les concepteurs ne sachant pas à l’avance à quelles fins il sera utilisé. Une grande partie de ces systèmes embarqués ont des contraintes temporelles très fortes, ce qui les distingue encore plus des ordinateurs grand public. A titre d’exemple, lorsqu’un conducteur de voiture freine brusquement, l’ordinateur de bord déclenche l’application ABS et il est primordial que cette application soit traitée endéans une courte échéance. Autrement dit, cette fonctionnalité ABS doit être traitée prioritairement par rapport aux autres fonctionnalités du véhicule. Ce type de système embarqué est alors dit “temps réel”, dû à ces notions de temps et de priorités entre les applications. La problèmatique posée par les systèmes temps réel est la suivante. Comment déterminer, à tout moment, un ordre d’exécution des différentes fonctionnalités de telle sorte qu’elles soient toutes exécutées entièrement endéans leur échéance ? De plus, avec l’apparition récente des systèmes multiprocesseurs, cette problématique s’est fortement complexifiée, vu que le système doit à présent déterminer quelle fonctionnalité s’exécute à quel moment sur quel processeur afin que toutes les contraintes temporelles soient respectées. Pour finir, ces systèmes embarqués temp réel multiprocesseurs se sont rapidement retrouvés confrontés à un problème de consommation d’énergie. Leur demande en terme de performance (et donc en terme d’énergie) à évolué beaucoup plus rapidement que la capacité des batteries qui les alimentent. Ce problème est actuellement rencontré par de nombreux systèmes, tels que les téléphones portables par exemple. L’objectif de cette thèse est de parcourir les différents composants de tels système embarqués et de proposer des solutions afin de réduire leur consommation d’énergie.

La planificación de la producción en plantas de proceso discontinuo es uno de los problemas más complejos e importantes para una amplia variedad de procesos industriales. A pesar de esta importancia la planificación de la producción es habitualmente un proceso manual que puede conducir a un exceso de inventario, una utilización ineficiente del capital y aumento en costes de producción.Este problema ha sido el sujeto de un importante esfuerzo investigador en los últimos años, especialmente desde principios de los 80 hasta la actualidad, aunque la industria se ha mostrado interesada en el problema desde los años 40. Durante este tiempo se ha realizado mucha investigación al respecto, pero la naturaleza compleja de problema hace que todavía no exista una solución aceptada ampliamente en la industria.Esta tesis describe un entorno genérico para la planificación de la producción en plantas de proceso discontinuo. Se han desarrollado diferentes componentes: un modelo de datos, un modelo de temporización, estrategias de asignación y secuenciación y diferentes alternativas de optimización.Uno de los aspectos más importantes del entorno presentado es su modularidad. El hecho de dividir el problema de planificación de la producción en diferentes módulos que comparten un modelo de datos común facilita la reutilización y la adaptación a escenarios industriales de las diferentes técnicas desarrolladas escogiendo la mejor alternativa para cada uno de ellos.El modelo de información orientado a objetos que se presenta en esta tesis permite la organización sistemática de la información de planta, permitiendo una representación detallada de las restricciones presentes en la industria.Por otra parte, el modelo de temporización de operaciones (EON) desarrollado en la presente tesis es la capacidad de representar restricciones temporales complejas presentes en la industria utilizando componentes sencillos. Se ha desarrollado una metodología para generar modelos EON a partir del modelo de información utilizado incluyendo restricciones de depósitos y restricciones temporales entre operaciones. Adicionalmente, un método iterativo permite tener en cuenta otros recursos limitantes dependientes de calendario, como mano de obra, electricidad, etc.En relación a las decisiones de nivel superior, se han desarrollado también reglas de balance de materiales, asignación y secuenciación que permiten obtener de una forma rápida y sencilla planes factibles a partir de un conjunto de demandas. Estas reglas se pueden aplicar tanto a planes de producción vacíos en situaciones de puesta en marcha de la planta, como a planes parcialmente llenos con la información de lotes que se están ejecutando en planta, lo que permite la replanificación en linea en caso de ser necesario.También se han aplicado diferentes técnicas de optimización a fin de mejorar planes de producción. Se han probado tanto métodos heurísticos como modelización matemática.En lo referente a los métodos heurísticos, se ha desarrollado un nuevo método de optimización (MSES) que mejora algunos aspectos referentes al algoritmo estándar de recocido simulado. Los algoritmos genéticos han sido también objeto de estudio, incorporando un algoritmo que transforma los individuos infactibles en factibles. Todos estos métodos han sido adaptados al entorno desarrollado permitiendo cambios de secuencia y asignación.En lo que respecta a la modelización matemática, se ha desarrollado un nuevo modelo MILP basado en una extensión del EON introduciendo variables de decisión de secuencia y asignación así como restricciones asociadas a almacenamientos intermedios.El entorno desarrollado en esta tesis ha sido aplicado a diferentes entornos industriales, proporcionando una validación de las tecnologías y modelos desarrollados. En todos los casos estudiados se han podido obtener planes de producción que cumplen con las restricciones presentes en planta, lo que permite establecer la validez de las metodologías desarrolladas para la planificación de la producción en plantas químicas de proceso discontinuo. / The scheduling of batch processes is one of the most complex and important problems faced by a wide variety of processing industries. In spite of this importance, scheduling is often a manual procedure, which leads to operation characterized by high inventories, inefficient capital utilization and increased operation costs. There are also reported complains about the lack of powerful, easy-to-use, PC based tools able to solve detailed operational problems, as well as perform high level analysis across the supply chain.This problem has been the focus of an important amount of research work in the recent years, especially from the early 1980's to nowadays, although the industry has been interested in effective ways of solving the scheduling problem since the early 1940's. An extensive work has been done but the complex nature of the scheduling problem results on the lack of a unique solution widely accepted in the industry.This thesis describes a global generic framework for planning and scheduling of batch chemical plants. Different components have been studied: a data model, a timing model, heuristic sequencing and assignment strategies and optimization procedures.One of the strongest points of the framework presented is its modularity. The fact of having the different components of planning and scheduling as separate modules sharing a common data model allows an easy use and adaptation of different techniques that can help solving the scheduling and planning problem in specific cases. This modular approach has been useful when applying the techniques presented to industrial scenarios. Adaptation to specific scenarios choosing the best alternative for each one is not only possible but also easy.The key point for achieving this is to share the common data and timing model (the EON model). The extensible object oriented data model presented in this thesis allows an organized and systematic information management dealing with the detailed representation of batch processes in the chemical industry. The main strength of the EON model is the capability of representation of complex time constraints between operations in the same schedule using simple components. EON model is presented and developed in detail. A methodology for the representation of storage constraints as time constraints as EON constraints is also presented. An iterative procedure allows also to take into account of limited resources as manpower, electricity, etc.Dispatching-like rules have been developed for the calculation of the material balances, the unit assignment and the batch sequencing. The strength of this approach is based in the easy implementation and adaptation to a batch oriented framework. These rules can be applied to empty schedules or to schedules that already contain frozen batches, which represents the actual situation in the plant. This last aspect allows the use of this kind of rules when performing on-line scheduling.Different optimization techniques have been used in this thesis to solve the scheduling approach presented. Stochastic and mathematical methods have been used and tested.Regarding to the stochastic methods, a new optimization algorithm (MSES) has been introduced that improves the performance of the SA standard algorithm. A modified GA algorithm has also been proposed that transforms the infeasible sequences commonly generated into feasible ones. All the stochastic methods used were adapted to batch processing structures involving batch sequencing and rule driven unit assignment.Regarding to the mathematical approach, the mathematical formulation presented in the EON timing model has been extended by introducing sequence and assignment variables as well as storage constraints.The framework developed in this thesis has been successfully applied to different industrial scenarios that are shown. The proposed solutions have been able to represent all the complexity of the test cases studied providing a powerful tool for planning and scheduling of the different plants.

In this paper we propose an improved algorithm to search optimal solutions to the single machine total weighted tardiness scheduling problem. We propose using longest common sequence to combine with the random key method. Numerical simulation shows that the scheme we proposed could improve the search efficiency of genetic algorithm in this problem for some cases.

Partitioned preemptive EDF scheduling is very similar to bin packing, but there is a subtle difference. Estimating the probability of schedulability under a given total utilization has been studied empirically before. Here, we show an approach for closed-form formulae for the problem, starting with n = 3 tasks on m = 2 processors.