Applying backfilling over a non-dedicated cluster

The resource utilization level in open laboratories of several universities has been shown to be very low. Our aim is to take advantage of those idle resources for parallel computation without disturbing the local load. In order to provide a system that lets us execute parallel applications in such a non-dedicated cluster, we use an integral scheduling system that considers both Space and Time sharing concerns. For dealing with the Time Sharing (TS) aspect, we use a technique based on the communication-driven coscheduling principle. This kind of TS system has some implications on the Space Sharing (SS) system, that force us to modify the way job scheduling is traditionally done. In this paper, we analyze the relation between the TS and the SS systems in a non-dedicated cluster. As a consequence of this analysis, we propose a new technique, termed 3DBackfilling. This proposal implements the well known SS technique of backfilling, but applied to an environment with a MultiProgramming Level (MPL) of the parallel applications that is greater than one. Besides, 3DBackfilling considers the requirements of the local workload running on each node. Our proposal was evaluated in a PVM/MPI Linux cluster, and it was compared with several more traditional SS policies applied to non-dedicated environmentsVI Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

CISNE-P: a global scheduling oriented to now environments

In this work, we present an integral scheduling system for non-dedicated clusters, termed CISNE-P, which ensures the performance required by the local applications, while simultaneously allocating cluster resources to parallel jobs. Our approach solves the problem efficiently by using a social contract technique. This kind of technique is based on reserving computational resources, preserving a predetermined response time to local users. CISNE-P is a middleware which includes both a previously developed space-sharing job scheduler and a dynamic coscheduling system, a time sharing scheduling component. The experimentation performed in a Linux cluster shows that these two scheduler components are complementary and a good coordination improves global performance significantly. We also compare two different CISNE-P implementations: one developed inside the kernel, and the other entirely implemented in the user space.Facultad de Informátic

Applying backfilling over a non-dedicated cluster

The resource utilization level in open laboratories of several universities has been shown to be very low. Our aim is to take advantage of those idle resources for parallel computation without disturbing the local load. In order to provide a system that lets us execute parallel applications in such a non-dedicated cluster, we use an integral scheduling system that considers both Space and Time sharing concerns. For dealing with the Time Sharing (TS) aspect, we use a technique based on the communication-driven coscheduling principle. This kind of TS system has some implications on the Space Sharing (SS) system, that force us to modify the way job scheduling is traditionally done. In this paper, we analyze the relation between the TS and the SS systems in a non-dedicated cluster. As a consequence of this analysis, we propose a new technique, termed 3DBackfilling. This proposal implements the well known SS technique of backfilling, but applied to an environment with a MultiProgramming Level (MPL) of the parallel applications that is greater than one. Besides, 3DBackfilling considers the requirements of the local workload running on each node. Our proposal was evaluated in a PVM/MPI Linux cluster, and it was compared with several more traditional SS policies applied to non-dedicated environmentsVI Workshop de Procesamiento Distribuido y Paralelo (WPDP)Red de Universidades con Carreras en Informática (RedUNCI

What to consider for applying backfilling on non-dedicated environments

The resource utilization level in open laboratories of several universities has been shown to be very low. Our aim is to take advantage of those idle resources for parallel computation without disturbing the local load. In order to provide a system that lets us execute parallel applications in such a non-dedicated cluster, we use an integral scheduling system that considers both Space and Time sharing concerns. For dealing with the Time Sharing (TS) aspect, we use a technique based on the communicationdriven coscheduling principle. This kind of TS system has some implications on the Space Sharing (SS) system, that force us to modify the way job scheduling is traditionally done. In this paper, we analyze the relation between the TS and the SS systems in a non-dedicated cluster. As a consequence of this analysis, we propose a new technique, termed 3DBackfilling. This proposal implements the well known SS technique of backfilling, but applied to an environment with a MultiProgramming Level (MPL) of the parallel applications that is greater than one. Besides, 3DBackfilling considers the requirements of the local workload running on each node. Our proposal was evaluated in a PVM/MPI Linux cluster, and it was compared with several more traditional SS policies applied to non-dedicated environments.Facultad de Informátic

Job scheduling considering best-effort and soft real-time applications on non-dedicated clusters

As Network Of Workstations (NOWs) emerge as a viable platform for a wide range of workloads, new scheduling approaches are needed to allocate the collection of resources from competing applications. New workload types introduce high uncertainty into the predictability of the system, hindering the applicability of the job scheduling strategies. A new kind of parallel applications has appeared in business or scientific domains, namely Soft Real-Time (SRT). They, together with new SRT desktop applications, turn prediction into a more difficult goal by adding inherent complexity to estimation procedures. In previous work, we introduced an estimation engine into our job scheduling system, termed CISNE. In this work, the estimation engine is extended, by adding two new kernels, both SRT aware. Experimental results confirm the better performance of simulated respect to the analytical kernels and show a maximum average prediction error deviation of 20%.Mientras las Redes de Estaciones de Trabajo (NOWs) emergen como una plataforma viable para un amplio espectro de aplicaciones, son necesarios nuevos enfoques para planificar los recursos disponibles entre las aplicaciones que compiten por ellos. Los nuevos tipos de cargas introducen una alta incertidumbre en la predictibilidad del sistema, afectando la aplicabilidad de las estrategias de planificación de tareas. Un nuevo tipo de aplicaciones paralelas, denominado tiempo real débil (SRT), ha aparecido tanto en los ámbitos comerciales como científicos. Las nuevas aplicaciones paralelas SRT, conjuntamente con los nuevos tipos de aplicaciones SRT de escritorio, convierten la predicción en una meta aún más difícil, al agregar complejidad a los procedimientos de estimación. En trabajos anteriores dotamos al sistema CISNE de un motor de estimación. En este trabajo añadimos al sistema de predicción fuera de línea dos nuevos núcleos de estimación con capacidad SRT. Los resultados experimentales muestran un mejor rendimiento del núcleo simulado con respecto a su homólogo analítico, mostrando un promedio de desviación máximo del 20%.VIII Workshop de Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informática (RedUNCI

Job scheduling considering best-effort and soft real-time applications on non-dedicated clusters

As Network Of Workstations (NOWs) emerge as a viable platform for a wide range of workloads, new scheduling approaches are needed to allocate the collection of resources from competing applications. New workload types introduce high uncertainty into the predictability of the system, hindering the applicability of the job scheduling strategies. A new kind of parallel applications has appeared in business or scientific domains, namely Soft Real-Time (SRT). They, together with new SRT desktop applications, turn prediction into a more difficult goal by adding inherent complexity to estimation procedures. In previous work, we introduced an estimation engine into our job scheduling system, termed CISNE. In this work, the estimation engine is extended, by adding two new kernels, both SRT aware. Experimental results confirm the better performance of simulated respect to the analytical kernels and show a maximum average prediction error deviation of 20%.Mientras las Redes de Estaciones de Trabajo (NOWs) emergen como una plataforma viable para un amplio espectro de aplicaciones, son necesarios nuevos enfoques para planificar los recursos disponibles entre las aplicaciones que compiten por ellos. Los nuevos tipos de cargas introducen una alta incertidumbre en la predictibilidad del sistema, afectando la aplicabilidad de las estrategias de planificación de tareas. Un nuevo tipo de aplicaciones paralelas, denominado tiempo real débil (SRT), ha aparecido tanto en los ámbitos comerciales como científicos. Las nuevas aplicaciones paralelas SRT, conjuntamente con los nuevos tipos de aplicaciones SRT de escritorio, convierten la predicción en una meta aún más difícil, al agregar complejidad a los procedimientos de estimación. En trabajos anteriores dotamos al sistema CISNE de un motor de estimación. En este trabajo añadimos al sistema de predicción fuera de línea dos nuevos núcleos de estimación con capacidad SRT. Los resultados experimentales muestran un mejor rendimiento del núcleo simulado con respecto a su homólogo analítico, mostrando un promedio de desviación máximo del 20%.VIII Workshop de Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informática (RedUNCI

Performance measurement and analysis of PC based cluster server using SET of Architecture and modeling a scalable High performance cluster

Not availabl

Planificación de aplicaciones best-effort y soft real-time en NOWs

La aparición de nuevos tipos de aplicaciones, como vídeo bajo demanda, realidad virtual y videoconferencias entre otras, caracterizadas por la necesidad de cumplir sus deadlines. Este tipo de aplicaciones, han sido denominadas en la literatura aplicaciones soft-real time (SRT) periódicas. Este trabajo se centra en el problema de la planificación temporal de este nuevo tipo de aplicaciones en clusters no dedicados.L'aparició de nous tipus d'aplicacions, com vídeo sota demanda, realitat virtual i videoconferències entre unes altres, caracteritzades per la necessitat de complir les seves deadlines. Aquest tipus d'aplicacions, han estat denominades en la literatura aplicacions soft-real time (SRT) periòdiques. Aquest treball es centra en el problema de la planificació temporal d'aquest nou tipus d'aplicacions en clusters no dedicats

Manejo de históricos en clusters

CISNE es un sistema de cómputo en paralelo del Departamento de Arquitectura de Computadores y Sistemas Operativos (DACSO). Para poder implementar políticas de ordenacción de colas y selección de trabajos, este sistema necesita predecir el tiempo de ejecución de las aplicaciones. Con este trabajo se pretende proveer al sistema CISNE de un método para predecir el tiempo de ejecución basado en un histórico donde se almacenarán todos los datos sobre las ejecuciones.CISNE és un sistema de còmput en paral·lel del Departament d'Arquitectura de Computadors i Sistemes Operatius (DACSO). Per poder implementar polítiques d'ordenació de cues i selecció de treballs, aquest sistema necessita predir el temps d'execució de les aplicacions. Amb aquest treball es pretén proveir el sistema CISNE amb un mètode per predir el temps d'execució basat en un històric on s'emmgatzemaran totes les dades sobre les execucions.CISNE is a parallel computing system of the Department of Architecture of Computers and Operating Systems (DACSO). For being able to implement politics of queues ordering and job selection, this system needs to predict the execution time of the applications. With this work, it is intended to supply to the system CISNE of a method to predict the execution time based on one historical where all the data about executions will be stored