网格环境下基于动态粗糙集的资源匹配算法
Resource Matchmaking Algorithm using Dynamic Rough Set in Grid Environment
摘要：
网格环境是一个面向服务的基础设施，在这个基础设施中，许多异构的资源参与提供高性能的计算。网格环境中的一个缺陷是通告资源和请求资源之间的不确定性和不确定性。此外，在诸如网格的环境中，动态性被认为是必须处理的关键问题。经典粗糙集理论已经被用来处理不确定性和不确定性问题。但它只能用于静态系统，不能支持系统的动态性。在这项工作中，我们提出了一种解决方案，称为动态粗糙集资源发现（DRSRD），用于处理的情况下，基于动态粗糙集理论，考虑在这种环境中的动态功能的不确定性和不确定性问题。以这种方式，所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名过程。我们还报告了从GridSim模拟器中获得的解决方案的结果。比较了DRSRD算法、经典的基于粗糙集理论的算法以及DRSRD与OWL S的组合算法。DRSRD算法在网格等动态系统中具有复杂性和不确定性的情况下，比经典的基于粗糙集理论的算法，以及OWL S和RISK相结合的算法具有更好的精度。
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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