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In this paper we describe the Pegasus system that can map complex workflows onto the Grid. Pegasus takes an abstract description of a workflow and finds the appropriate data and Grid resources to execute the workflow. Pegasus is being released as part of the GriPhyN Virtual Data Toolkit and has been used in a variety of applications ranging from astronomy,(More)
Grid applications require allocating a large number of heterogeneous tasks to distributed resources. A good allocation is critical for efficient execution. However, many existing grid toolkits use matchmaking strategies that do not consider overall efficiency for the set of tasks to be run. We identify two families of resource allocation algorithms:(More)
In this paper we address the problem of automatically generating job workflows for the Grid. These workflows describe the execution of a complex application built from individual application components. In our work we have developed two workflow generators: the first (the Concrete Workflow Generator CWG) maps an abstract workflow defined in terms of(More)
Planning is a complex reasoning task that is well suited for the study of improving performance and knowledge by learning, i.e. by accumulation and interpretation of planning experience. PRODIGY is an architecture that integrates planning with multiple learning mechanisms. Learning occurs at the planner’s decision points and integration in PRODIGY is(More)
A key challenge for grid computing is creating large-scale, end-to-end scientific applications that draw from pools of specialized scientific components to derive elaborate new results. We develop Pegasus, an AI planning system which is integrated into the grid environment that takes a user's highly specified desired results, generates valid workflows that(More)
Planning, the process of nding a course of action which can be executed to achieve some goal, is an important and well-studied area of AI. One of the central assumptions of classical AI-based planning is that after performing an action the resulting state can be predicted completely and with certainty. This assumption has allowed the development of planning(More)
We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (a) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills,(More)
ions and hierarchical approaches While the envelope extension method ignores portions of the state space, other techniques have considered abstractions of the state space that try to group together sets of states that behave similarly under the chosen actions of the optimal policy. Boutilier and Dearden (Boutilier & Dearden 1994) assume a representation for(More)
Recently, several researchers have demonstrated domains where partially-ordered planners outperform totally-ordered planners. In (Barrett & Weld 1994), Barrett and Weld build a series of arti cial domains exploring the concepts of trivial and laborious serializability, in which a partially-ordered planner, snlp, consistently outperforms two totally-ordered(More)