The increasing complexity, heterogeneity, and dynamism of emerging pervasive Grid environments and applications has necessitated the development of autonomic self-managing solutions, that are inspired by biological systems and deal with similar challenges of complexity, heterogeneity, and uncertainty. This paper introduces Project AutoMate and describes its key components. The overall goal of Project Automate is to investigate conceptual models and implementation architectures that can enable the development and execution of such self-managing Grid applications. Illustrative autonomic scientific and engineering Grid applications enabled by AutoMate are presented.
SUMMARYThis paper presents the design, implementation, and deployment of the DISCOVER Web-based computational collaboratory. Its primary goal is to bring large distributed simulations to the scientists'/engineers' desktop by providing collaborative Web-based portals for monitoring, interaction and control. DISCOVER supports a three-tier architecture composed of detachable thin-clients at the frontend, a network of interaction servers in the middle, and a control network of sensors, actuators, and interaction agents at the back-end. The interaction servers enable clients to connect and collaboratively interact with registered applications using a browser. The application control network enables sensors and actuators to be encapsulated within, and directly deployed with the computational objects. The application interaction gateway manages overall interaction. It uses Java Native Interface to create Java proxies that mirror computational objects and allow them to be directly accessed at the interaction server. Security and authentication are provided using customizable access control lists and SSL-based secure servers. Copyright 2001 John Wiley & Sons, Ltd.KEY WORDS: Web-based computational collaboratory; computational interaction and steering
Abstract. The adequate location of wells in oil and environmental applications has a significant economic impact on reservoir management. However, the determination of optimal well locations is both challenging and computationally expensive. The overall goal of this research is to use the emerging Grid infrastructure to realize an autonomic self-optimizing reservoir framework. In this paper, we present a policy-driven peer-to-peer Grid middleware substrate to enable the use of the Simultaneous Perturbation Stochastic Approximation (SPSA) optimization algorithm, coupled with the Integrated Parallel Accurate Reservoir Simulator (IPARS) and an economic model to find the optimal solution for the well placement problem.
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