5492949
Resource Matching and a Matchmaking Service for an Intelligent Grid
We discuss the application of matching in the area of resource discovery and resource allocation in grid computing. We present a formal definition of matchmaking, overview algorithms to evaluate different matchmaking expressions, and develop a matchmaking service for an intelligent grid environment. Keywords—Grid, Matchmaking, Ontology
2884857
A genetic approach to planning in heterogeneous computing environments
Planning is an artificial intelligence problem with a wide range of real-world applications. Genetic algorithms, neural networks, and simulated annealing are heuristic search methods often used to solve complex optimization problems. In this paper, we propose a genetic approach to planning in the context of workflow management and process coordination on a heterogenous grid. We report results for two planning problems, the Towers of Hanoi and the Sliding-tile puzzle.
18463797
Planning with Recursive Subgoals
In this paper, we introduce an effective strategy for subgoal division and ordering based upon recursive subgoals and combine this strategy with a genetic-based planning approach. This strategy can be applied to domains with conjunctive goals. The main idea is to recursively decompose a goal into a set of serializable subgoals and to specify a strict ordering among the subgoals. Empirical results show that the recursive subgoal strategy reduces the size of the search space and improves the quality of solutions to planning problems.
7221846
Evolving Sensor Suites for Enemy Radar Detection
Designing optimal teams of sensors to detect the enemy radars for military operations is a challenging design problem. Many applications require the need to manage sensor resources. There is a tradeoff between the need to decrease the cost and to increase the capabilities of a sensor suite. In this paper, we address this design problem using genetic algorithms. We attempt to evolve the characteristics, size, and arrangement of a team of sensors, focusing on minimizing the size of sensor suite while maximizing its detection capabilities. The genetic algorithm we have developed has produced promising results for different environmental configurations as well as varying sensor resources.
10203274
Adaptation of Length in a Nonstationary Environment
In this paper, we examine the behavior of a variable length GA in a nonstationary problem environment. Results indicate that a variable length GA is better able to adapt to changes than a fixed length GA. Closer examination of the evolutionary dynamics reveals that a variable length GA can in fact take advantage of its variable length representation to exploit good quality building blocks after a change in the problem environment.
46532464
Lagrangian dynamics and analysis of a hybrid linkage system
15030041
Adaptive reduction parallelization techniques
In this paper, we propose to adapt parallelizing transformations, more specifically, reduction parallelizations, to the actual reference pattern executed by a loop, i.e., to the particular input data and dynamic phase of a program. More precisely we will show how, after validating a reduction at run-time (when this is not possible at compile time) we can dynamically characterize its reference pattern and choose the most appropriate method for parallelizing it. For this purpose, we develop a library of parallel reduction algorithms, including both previously known and novel schemes, which includes algorithms specialized for different classes of access behavior. In particular, each algorithm in our library has identified strengths related to specific reference pattern characteristics, which are matched, at run-time, with measured characteristics of the actual reference pattern. The matching of algorithm to reference pattern is performed using a decision-tree based selection scheme. The contribution of this work consists in new optimizations for reduction parallelization and in the introduction of a new approach to the optimization of irregular applications: Characteristic based customization.
7547507
Efficient Numerical Error Bounding for Replicated Network Services
The goal of this work is to support replicated network services that accept updates to numerical records from multiple wide-area locations. Given the high overhead of maintaining strong consistency, many replicated services can tolerate divergence of their shared data, as long as the numerical error is bounded. Target distributed services include replicated stock quotes services, online auctions, distributed sensor systems, wide-area resource accounting and load balancing for replicated servers. We present two algorithms to e ciently bound absolute error using only local information. Split-Weight AE separately bounds increases and decreases, while Compound-Weight AE bounds them together. The two algorithms can be combined to provide good performance and low space overhead. Our Inductive RE algorithm transforms relative error to absolute error solely based on local knowledge, taking advantage of the fact that the divergence was properly bounded prior to each invocation of the algorithm. We also discuss two optimizations that reduce the space and computational overheads in the algorithms. This work is supported in part by the National Science Foundation (EIA-99772879). Vahdat is also supported by an NSF CAREER award (CCR-9984328). Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 26th VLDB Conference, Cairo, Egypt, 2000.
6049546
Persistent applications via automatic recovery
Building highly available enterprise applications using Web-oriented middleware is hard. Runtime implementations frequently do not address the problems of application state persistence and fault-tolerance, placing the burden of managing session state and, in particular, handling system failures on application programmers. This paper describes Phoenix/APP, a runtime service based on the notion of recovery guarantees. Phoenix/APP transparently masks failures and automatically recovers component-based applications. This both increases application availability and simplifies application development. We demonstrate the feasibility of this approach by describing the design and implementation of Phoenix/APP in Microsoft's .NET runtime and present results on the cost of persisting and recovering component-based applications.
23563245
Estimating fuzzy phoneme similarity relations for continuous speech recognition