3 research outputs found

    Statistic Rate Monotonic Scheduling

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    In this paper we present Statistical Rate Monotonic Scheduling (SRMS), a generalization of the classical RMS results of Liu and Layland that allows scheduling periodic tasks with highly variable execution times and statistical QoS requirements. Similar to RMS, SRMS has two components: a feasibility test and a scheduling algorithm. The feasibility test for SRMS ensures that using SRMS' scheduling algorithms, it is possible for a given periodic task set to share a given resource (e.g. a processor, communication medium, switching device, etc.) in such a way that such sharing does not result in the violation of any of the periodic tasks QoS constraints. The SRMS scheduling algorithm incorporates a number of unique features. First, it allows for fixed priority scheduling that keeps the tasks' value (or importance) independent of their periods. Second, it allows for job admission control, which allows the rejection of jobs that are not guaranteed to finish by their deadlines as soon as they are released, thus enabling the system to take necessary compensating actions. Also, admission control allows the preservation of resources since no time is spent on jobs that will miss their deadlines anyway. Third, SRMS integrates reservation-based and best-effort resource scheduling seamlessly. Reservation-based scheduling ensures the delivery of the minimal requested QoS; best-effort scheduling ensures that unused, reserved bandwidth is not wasted, but rather used to improve QoS further. Fourth, SRMS allows a system to deal gracefully with overload conditions by ensuring a fair deterioration in QoS across all tasks---as opposed to penalizing tasks with longer periods, for example. Finally, SRMS has the added advantage that its schedulability test is simple and its scheduling algorithm has a constant overhead in the sense that the complexity of the scheduler is not dependent on the number of the tasks in the system. We have evaluated SRMS against a number of alternative scheduling algorithms suggested in the literature (e.g. RMS and slack stealing), as well as refinements thereof, which we describe in this paper. Consistently throughout our experiments, SRMS provided the best performance. In addition, to evaluate the optimality of SRMS, we have compared it to an inefficient, yet optimal scheduler for task sets with harmonic periods.National Science Foundation (CCR-970668

    Analizar, dise帽ar e implementar un sistema de informaci贸n que soporte el proceso de Gesti贸n de Solicitudes de Servicio del Ministerio de Comunicaciones de una Iglesia Evang茅lica

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    La Iglesia Evang茅lica caso de estudio es una entidad religiosa conformada por personas vinculadas por una misma fe en Cristo. La iglesia (en adelante, tambi茅n denominada "la organizaci贸n") tiene por misi贸n evangelizar a las comunidades con las que tiene contacto, transmitiendo las ense帽anzas seg煤n la Biblia. En ese sentido, la iglesia ha experimentado crecimiento durante los 煤ltimos a帽os en n煤mero de miembros, cantidad de sedes, y actividades internas y externas. Actualmente cuenta con 13 localidades y un aproximado de 6 mil miembros. El principal proceso de la organizaci贸n es la Evangelizaci贸n, que consiste en la comunicaci贸n de los principios b铆blicos a sus comunidades cercanas. Dicho proceso es soportado por distintas actividades, entre las que se encuentran reuniones de celebraci贸n, dictado de cursos, retiros, entre otros. Estas actividades son organizadas por alguna de las 谩reas de la organizaci贸n, seg煤n la competencia de cada una. Para fines de este plan de proyecto, se denomina tambi茅n Ministerio a cada una de dichas 谩reas. Durante la organizaci贸n de estas actividades, se debe lograr la difusi贸n y cobertura de las mismas, tarea a cargo del Ministerio de Comunicaciones. El ministerio tiene por tarea elaborar publicidad, realizar cobertura audiovisual, y/o crear piezas gr谩ficas de mayor complejidad. Actualmente, existen procesos y estructuras organizacionales que soportan estas labores, aunque muchos de estos procesos son manuales o de alta intervenci贸n humana. El Ministerio de Comunicaciones recepciona las solicitudes de servicio a trav茅s de correo electr贸nico, las registra en hojas de c谩lculo, asigna un colaborador responsable, un equipo de trabajo y planifica la entrega en un cronograma. Debido a la cantidad de solicitudes recibidas, este trabajo es lento, y altamente expuesto a errores y p茅rdidas de informaci贸n. Adem谩s, el almacenamiento en hojas de c谩lculo hace dif铆cil la extracci贸n de informaci贸n y la generaci贸n de reportes, lo que conlleva a un uso poco eficiente de recursos. Se observa que tambi茅n es requerido un proceso de planificaci贸n que permita priorizar las solicitudes recibidas dado que se cuenta con recursos de producci贸n limitados. La planificaci贸n actualmente se realiza procesando las solicitudes en una cola (el primero en entrar, el primero en salir), y considerando una fecha de cierre semanal y un lapso de 15 d铆as para la entrega. Sin embargo, este m茅todo de planificaci贸n no siempre prioriza las solicitudes correctamente ni otorga una estimaci贸n suficiente para la entrega. Por todo lo mencionado, el Ministerio de Comunicaciones reconoce la dificultad existente en su proceso de gesti贸n de solicitudes de servicio, y desea reducir dicha dificultad, de manera que pueda brindar servicios internos a los dem谩s ministerios de la iglesia con eficiencia y eficacia. Por estos motivos, una de las acciones que la organizaci贸n desea ejecutar es elaborar un sistema de informaci贸n que permita registrar las solicitudes de servicio, gestionar dichas solicitudes y generar reportes con informaci贸n id贸nea para el 谩rea y la iglesia. Asimismo, se requiere de un m茅todo adecuado para la planificaci贸n de las entregas de cada solicitud, para lo cual se har谩 uso de un algoritmo heur铆stico. De esta manera, se busca que el sistema constituya una herramienta de soporte para la gesti贸n de solicitudes de servicio.Tesi

    Parallel replication for distributed video-on-demand systems.

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    Lie, Wai-Kwok Peter.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 79-83).Abstract --- p.iAcknowledgments --- p.iiChapter 1 --- Introduction --- p.1Chapter 2 --- Background & Related Work --- p.5Chapter 2.1 --- Early Work on Multimedia Servers --- p.6Chapter 2.2 --- Compression of Multimedia Data --- p.6Chapter 2.3 --- Multimedia File Systems --- p.7Chapter 2.4 --- Scheduling Support for Multimedia Systems --- p.8Chapter 2.5 --- Inter-media Synchronization --- p.9Chapter 2.6 --- Related Work on Replication in VOD Systems --- p.9Chapter 3 --- System Model --- p.12Chapter 4 --- Replication Methodology --- p.15Chapter 4.1 --- Replication Triggering Policy --- p.16Chapter 4.2 --- Source & Target Nodes Selection Policies --- p.17Chapter 4.3 --- Replication Policies --- p.18Chapter 4.3.1 --- Policy 1: Injected Sequential Replication --- p.20Chapter 4.3.2 --- Policy 2: Piggybacked Sequential Replication --- p.22Chapter 4.3.3 --- Policy 3: Injected Parallel Replication --- p.25Chapter 4.3.4 --- Policy 4: Piggybacked Parallel Replication --- p.28Chapter 4.3.5 --- Policy 5: Injected & Piggybacked Parallel Replication --- p.34Chapter 4.3.6 --- Policy 6: Multi-Source Injected & Piggybacked Parallel Replication --- p.36Chapter 4.4 --- Dereplication Policy --- p.37Chapter 5 --- Distributed Architecture for VOD Server --- p.39Chapter 5.1 --- Server Node --- p.40Chapter 5.2 --- Movie Manager --- p.42Chapter 5.3 --- Metadata Manager --- p.42Chapter 5.4 --- Protocols for Distributed VOD Architecture --- p.43Chapter 5.4.1 --- Protocol for Servicing New Customers --- p.43Chapter 5.4.2 --- Protocol for Servicing Existing Customers --- p.45Chapter 5.4.3 --- Protocol for Single/Multi-Source Injected & Parallel Replication --- p.46Chapter 5.4.4 --- Protocol for Dereplication --- p.48Chapter 5.5 --- Failure Handling --- p.49Chapter 5.5.1 --- Handling of Server Node Failures --- p.50Chapter 5.5.2 --- Handling of Movie Manager Failures --- p.52Chapter 6 --- Results --- p.55Chapter 6.1 --- Performance Metric --- p.56Chapter 6.2 --- Simulation Environment --- p.58Chapter 6.3 --- Results of Experiments without Dereplication --- p.59Chapter 6.3.1 --- Comparison of Different Replication Policies --- p.60Chapter 6.3.2 --- Effect of Early Acceptance/Migration --- p.61Chapter 6.3.3 --- Answer to the Resources Consumption Tradeoff issue --- p.62Chapter 6.3.4 --- Effect of Varying Movie Popularity Skewness --- p.64Chapter 6.3.5 --- Effect of Varying Replication Threshold --- p.64Chapter 6.3.6 --- Comparison of Different Target Node Selection Policies --- p.65Chapter 6.4 --- Overall Impact of Dynamic Replication --- p.66Chapter 7 --- Comparison with BSR-based Policy --- p.71Chapter 8 --- Conclusions --- p.75Chapter 8.1 --- Summary --- p.75Chapter 8.2 --- Future Research Directions --- p.76Bibliography --- p.7
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