Today, mobile and smart phones are often viewed as enablers of pervasive computing systems because they provide anytime and anywhere access to information services and computational resources. However, mobile devices ...
详细信息
Today, mobile and smart phones are often viewed as enablers of pervasive computing systems because they provide anytime and anywhere access to information services and computational resources. However, mobile devices are inherently constrained in their computational power and battery capacity making them mere "dumb terminals" connected to a resource-rich pervasive environment. If they are ever to play a more prominent role as true elements of a pervasive environment, mobile devices must be able to embed more application logic and delegate processing requests to pervasive infrastructure. In this paper we discuss distribution and offloading of computationally intensive tasks in pervasive environments populated by mobile devices. This approach is illustrated by experimenting with a distributed version of iterative deepening A* search algorithm. In our approach, the solution space of a problem being solved is partitioned and distributed among heterogeneous mobile devices, which yields a significant increase in the time of finding an optimal solution. Distributed ida* search algorithm does not require any coordination or communication between mobile devices, but added inter-processor communication through shared memory further increases the efficiency of the algorithm. This paper presents the results of our experiments with the algorithm and discusses a number of issues related to its implementation.
In this paper, we study the performance of various ida*-style searches and investigate methods to improve their performance by predicting in each stage the threshold to be used for pruning. Without loss of generality,...
详细信息
In this paper, we study the performance of various ida*-style searches and investigate methods to improve their performance by predicting in each stage the threshold to be used for pruning. Without loss of generality, we consider minimization problems in this paper. We first present three models to approximate the distribution of the number of search nodes by lower bounds: exponential, geometric, and linear, and illustrate these distributions based on some well-known combinatorial search problems. Based on these distributions, we show the performance of an ideal ida* algorithm and identify reasons why existing ida*-style algorithms perform well. In practice, we will be able to know from experience the type of distribution for a given problem instance, but will not be able to know the parameters of this distribution until the instance is solved. Hence, we develop Rida*, a method that estimates dynamically the parameters of the distribution, and predicts the best threshold to be used in each stage. Finally, we compare the performance of several ida*-style algorithms—Korf’s ida* and RBFS, Rida*, ida*_CR and DFS*—on several application problems, and identify conditions under which each of these algorithms will perform well.
In this paper we describe a collection of heuristic search algorithms which use mixed ‘best-first’ and ‘depth-first’ strategies. These algorithms are designed to match the actual features of modern computers that ...
详细信息
暂无评论