Abstract
Grid environments are dynamic and heterogeneous by nature, therefore requiring adaptive scheduling strategies. Reinforcement learning is an interesting and simple adaptive approach that may work well in actual grid environments. In this work, we employ reinforcement learning to classify available resources in a grid environment, giving support to two scheduling algorithms, AG and MQD. We study the makespan optimisation and load balancing. An algorithm known as RR is used for normalising purposes. Copyright © 2009 Inderscience Enterprises Ltd.

Abstract
Several works on grid computing have been proposed in the last years. However, most of them, including available software, can not deal properly with some issues related to control of applications that spread a very large number of tasks across the grid network. This work presents a step toward solving the problem of controlling such applications. We propose and discuss an architectural model called GRAND (Grid Robust ApplicatioN Deployment) based on partitioning and hierarchical submission and control of such applications. The main contribution of our model is to be able to control the execution of a huge number of distributed tasks while preserving data locality and reducing the load of the submit machines. We propose a taxonomy to classify application models to run in grid environments and partitioning methods. We also present our application description language GRID-ADL. Copyright 2004 ACM.

Abstract
In this work, we study the behaviour of different resource scheduling strategies when doing job orchestration in grid environments. We empirically demonstrate that scheduling strategies based on Reinforcement Learning are a good choice to improve the overall performance of grid applications and resource utilization.

Abstract
In this paper we present the architecture for the Personal Autonomic Desktop Manager, a self managing application designed to act on behalf of the user in several aspects: protection, healing, optimization and configuration. The overall goal of this research is to improve the correlation of the autonomic self* properties and doing so also enhance the overall self-management capacity of the desktop (autonomicity). We introduce the Circulatory Computing (CC) model, a self-managing system initiative based on the biological metaphor of the cardiovascular system, and use its concepts in the design and implementation of the architecture.

Abstract
In this work we build the first BI-RADS parser for Portuguese free texts, modeled after existing approaches to extract BI-RADS features from English medical records. Our concept finder uses a semantic grammar based on the BI-RADS lexicon and on iterative transferred expert knowledge. We compare the performance of our algorithm to manual annotation by a specialist in mammography. Our results show that our parser's performance is comparable to the manual method. © 2012 IEEE.

Abstract
One of the challenges in Grid computing research is to provide a means to automatically submit, manage, and monitor applications whose main characteristic is to be composed of a large number of tasks. The large number of explicit tasks, generally placed on a centralized job queue, can cause several problems: (1) they can quickly exhaust the memory of the submission machine; (2) they can deteriorate the response time of the submission machine due to these demanding too many open ports to manage remote execution of each of the tasks; (3) they may cause network traffic congestion if all tasks try to transfer input and/or output files across the network at the same time; (4) they make it impossible for the user to follow execution progress without an automatic tool or interface; (5) they may depend on fault-tolerance mechanisms implemented at application level to ensure that all tasks terminate successfully. In this work we present and validate a novel architectural model, GRAND (Grid Robust ApplicatioN Deployment), whose main objective is to deal with the submission of a large numbers of tasks. Copyright (c) 2006 John Wiley & Sons, Ltd.