A Physical Particle and Plane Framework for Load Balancing in Multiprocessors

Different models for load balancing have been proposed before, each of which has its own features and advantages when considered for a specific scenario. Yet, nearly all of the existing techniques have assumed an oversimplified model of the system which is often not the case of the real world. In this paper, a new gradient based algorithm for dynamic load balancing on multiprocessors is proposed. This algorithm is an analogy of a classical physical model of a Particle & Plane system which operates based on the classic laws of physics dictated by the nature. 1

Decentralized load balancing in heterogeneous computational grids

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers. Although these are established research areas in parallel and distributed computing, grid computing environments present a number of new challenges, including large-scale computing resources, heterogeneous computing power, the autonomy of organisations hosting the resources, uneven job-arrival pattern among grid sites, considerable job transfer costs, and considerable communication overhead involved in capturing the load information of sites. This dissertation focuses on designing solutions for load balancing in computational grids that can cater for the unique characteristics of grid computing environments. To explore the solution space, we conducted a survey for load balancing solutions, which enabled discussion and comparison of existing approaches, and the delimiting and exploration of the apportion of solution space. A system model was developed to study the load-balancing problems in computational grid environments. In particular, we developed three decentralised algorithms for job dispatching and load balancing—using only partial information: the desirability-aware load balancing algorithm (DA), the performance-driven desirability-aware load-balancing algorithm (P-DA), and the performance-driven region-based load-balancing algorithm (P-RB). All three are scalable, dynamic, decentralised and sender-initiated. We conducted extensive simulation studies to analyse the performance of our load-balancing algorithms. Simulation results showed that the algorithms significantly outperform preexisting decentralised algorithms that are relevant to this research

High performance latent dirichlet allocation for text mining

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Latent Dirichlet Allocation (LDA), a total probability generative model, is a three-tier Bayesian model. LDA computes the latent topic structure of the data and obtains the significant information of documents. However, traditional LDA has several limitations in practical applications. LDA cannot be directly used in classification because it is a non-supervised learning model. It needs to be embedded into appropriate classification algorithms. LDA is a generative model as it normally generates the latent topics in the categories where the target documents do not belong to, producing the deviation in computation and reducing the classification accuracy. The number of topics in LDA influences the learning process of model parameters greatly. Noise samples in the training data also affect the final text classification result. And, the quality of LDA based classifiers depends on the quality of the training samples to a great extent. Although parallel LDA algorithms are proposed to deal with huge amounts of data, balancing computing loads in a computer cluster poses another challenge. This thesis presents a text classification method which combines the LDA model and Support Vector Machine (SVM) classification algorithm for an improved accuracy in classification when reducing the dimension of datasets. Based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the algorithm automatically optimizes the number of topics to be selected which reduces the number of iterations in computation. Furthermore, this thesis presents a noise data reduction scheme to process noise data. When the noise ratio is large in the training data set, the noise reduction scheme can always produce a high level of accuracy in classification. Finally, the thesis parallelizes LDA using the MapReduce model which is the de facto computing standard in supporting data intensive applications. A genetic algorithm based load balancing algorithm is designed to balance the workloads among computers in a heterogeneous MapReduce cluster where the computers have a variety of computing resources in terms of CPU speed, memory space and hard disk space

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