Queueing network models for load balancing in distributed systems

In distributed systems, load balancing can improve efficiency by migrating jobs from heavily loaded to lightly loaded sites. In this paper we present a method for optimal load allocation in a statistic environment. A queueing network model is used to evaluate response time; and mathematical programing techniques are used to find the load allocation that minimizes average response time. The method is not proposed as a substitute for dynamic, heuristic load balance policies; rather, it is preceived as a useful tool for resource allocation and capacity planning in distributed systems, and as a promising complement to dynamic policies in hibrid load balance strategies. The method can handle very general classes of problems, including: distinct classes of jobs, multitasking within each job, and; jobs with spawned tasks. Several examples illustrating these applications are reported.Em sistemas distribuídos, o balanceamento de carga pode melhorar a eficiência de um sistema se jobs executando em computadores com levada carga de trabalho forem transferidos para computadores com menor carga. Neste artigo, apresentamos um método para o balanceamento ótimo de carga em um ambiente estático. Um modelo de redes de filas é usado para avaliar o tempo de resposta e técnicas de programação matemática são usadas para se achar a alocação de carga que minimiza o tempo médio de resposta. O método não é proposto como substitutivo para políticas heurísticas dinâmicas de balanceamento de carga; entretanto, o método é visto como uma ferramenta útil para alocação de recursos e planejamento de capacidade em sistemas distribuídos, e como um complemento promissor a políticas dinâmicas em estratégias híbridas de balanceamento de carga

Task assignment in server farms under realistic workload conditions

Server farms have become very popular in recent years since they effectively address the problem of large delays, a common problem faced by many organisations whose systems receive high volumes of traffic. Recently, there has been a wide use of these server farms in two main areas, namely, Web hosting and scientific computing. The performance of such server farms is highly reliant on the underlying task assignment policy, a specific set of rules that defines how the incoming tasks are assigned to and processed at hosts. The aim of a task assignment policy is to optimise certain performance criteria such as the expected waiting time and slowdown. One of the key factors that affect the performance of these policies is the service time distribution of tasks. There is extensive evidence indicating that the service times of modern computer workloads closely follow heavy-tailed distributions that possess high variance. However, in certain environments, the service time distributions of tasks are unknown. Imposing parametric assumptions in such cases can lead to inaccurate and unreliable inferences. Considerable efforts have been made in recent years to devise efficient policies. Although these policies perform well under specific workload conditions, they have several major limitations. These include the assumption of known service times, inability to efficiently assign tasks in time sharing server farms, poor performance under changing workload conditions and poor performance under multiple server farms. This thesis aims at proposing novel task assignment policies for assigning tasks in server farms under two main classes of realistic workload conditions, namely, the heavy-tailed and arbitrary service time distributions. Arbitrary service time distributions are assumed, for cases where the underlying service time distribution of tasks is unknown. First we investigate ways to optimise the performance in a time-sharing server. We concentrate on a particular scheduling policy called multi-level time sharing policy (MLTP). We provide an extensive performance analysis of MTLP and show that MLTP can result in significant performance improvements under certain traffic conditions. Second we investigate how to improve the performance in time sharing server farms using MLTP. Three task assignment policies are proposed for time sharing server farms. Third we investigate how to design efficient task assignment policies to assign tasks in multiple server farms. We propose MCTPM which is based on a multi-tier host architecture. MCTPM supports preemptive task migration and it controls the traffic flow into server farms via a global dispatching device so as to optimise the performance. Finally, we investigate ways to design adaptive task assignment policies that make no assumptions regarding the underlying service time distribution of tasks. We propose a novel task assignment policy, called ADAPT-POLICY, which is based on a set of static-based task assignment policies. ADAPT-POLICY is based on a set of policies for the server farm and it adaptively changes the task assignment policy to suit the most recent traffic conditions. The experimental performance analysis of ADAPT-POLICY shows that ADAPT-POLICY outperforms other policies under a range of traffic conditions

Procesamiento paralelo : Balance de carga dinámico en algoritmo de sorting

Algunas técnicas de sorting intentan balancear la carga mediante un muestreo inicial de los datos a ordenar y una distribución de los mismos de acuerdo a pivots. Otras redistribuyen listas parcialmente ordenadas de modo que cada procesador almacene un número aproximadamente igual de claves, y todos tomen parte del proceso de merge durante la ejecución. Esta Tesis presenta un nuevo método que balancea dinámicamente la carga basado en un enfoque diferente, buscando realizar una distribución del trabajo utilizando un estimador que permita predecir la carga de trabajo pendiente. El método propuesto es una variante de Sorting by Merging Paralelo, esto es, una técnica basada en comparación. Las ordenaciones en los bloques se realizan mediante el método de Burbuja o Bubble Sort con centinela. En este caso, el trabajo a realizar -en términos de comparaciones e intercambios- se encuentra afectada por el grado de desorden de los datos. Se estudió la evolución de la cantidad de trabajo en cada iteración del algoritmo para diferentes tipos de secuencias de entrada, n datos con valores de a n sin repetición, datos al azar con distribución normal, observándose que el trabajo disminuye en cada iteración. Esto se utilizó para obtener una estimación del trabajo restante esperado a partir de una iteración determinada, y basarse en el mismo para corregir la distribución de la carga. Con esta idea, el métoEs revisado por: http://sedici.unlp.edu.ar/handle/10915/9500Facultad de Ciencias Exacta