基于动态粗糙集的网格资源匹配算法 环境

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摘要翻译：
网格环境是一个面向服务的基础设施，由许多异构资源参与提供高性能计算。网格环境中的bug问题之一是通告资源和请求资源之间的模糊性和不确定性。此外，在网格这样的环境中，动力性被认为是一个必须解决的关键问题。经典粗糙集被用来处理不确定性和模糊性。但它只能用于静态系统，不能支持系统的动态性。本文基于动态粗糙集理论，考虑环境中的动态特征，提出了一种解决模糊和不确定性问题的方法，称为动态粗糙集资源发现(DRSRD)。这样，请求的资源属性具有作为优先级的权重，根据该权重进行资源匹配和排序过程。并给出了在GridSim仿真器中进行仿真的结果。比较了基于粗糙集理论的经典算法DRSRD和UDDI与OWL相结合的算法。DRSRD算法对于网格等动态系统中具有模糊性和不确定性的情况，比传统的基于粗糙集理论的算法以及UDDI和OWL的组合算法具有更好的精度。
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英文标题：
《Resource Matchmaking Algorithm using Dynamic Rough Set in Grid
  Environment》
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作者：
Iraj Ataollahi, Mortza Analoui
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最新提交年份：
2009
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Distributed, Parallel, and Cluster Computing        分布式、并行和集群计算
分类描述：Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
包括容错、分布式算法、稳定性、并行计算和集群计算。大致包括ACM学科类C.1.2、C.1.4、C.2.4、D.1.3、D.4.5、D.4.7、E.1中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Grid environment is a service oriented infrastructure in which many heterogeneous resources participate to provide the high performance computation. One of the bug issues in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. Furthermore, in an environment such as grid dynamicity is considered as a crucial issue which must be dealt with. Classical rough set have been used to deal with the uncertainty and vagueness. But it can just be used on the static systems and can not support dynamicity in a system. In this work we propose a solution, called Dynamic Rough Set Resource Discovery (DRSRD), for dealing with cases of vagueness and uncertainty problems based on Dynamic rough set theory which considers dynamic features in this environment. In this way, requested resource properties have a weight as priority according to which resource matchmaking and ranking process is done. We also report the result of the solution obtained from the simulation in GridSim simulator. The comparison has been made between DRSRD, classical rough set theory based algorithm, and UDDI and OWL S combined algorithm. DRSRD shows much better precision for the cases with vagueness and uncertainty in a dynamic system such as the grid rather than the classical rough set theory based algorithm, and UDDI and OWL S combined algorithm.
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PDF链接：
https://arxiv.org/pdf/0909.1397

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关键词：粗糙集 Presentation Intelligence uncertainty environment 进行 权重 DRSRD vagueness 高性能