基于属性约简的应用服务器优化算法改进

优化参数配置是优化应用服务器性能的重要方面;基于传统参数调节的优化策略耗时耗力缺乏系统性和规律性;利用模块化思想针对目标决策函数对应用服务器参数进行分类,可构建条件属性约简模型;基于属性约简的应用服务器优化算法,可去除对于目标决策函数相对不重要的参数,并获得相对重要的参数,从而达到锁定目标重点调节,快速提高系统性能的目的;现有的约简模型优化算法多基于经典粗糙集理论,在等价关系的基础上构造分类,容易造成大量的信息破坏和流失;文章通过拓展等价关系到一般二元关系,利用广义粗糙集理论改良了基于模块化思想和属性约简模型的应用服务器优化算法,通过定义辨识函数对条件属性进行约简,再结合依赖度计算,得到最终目标参数。

Improved Algorithm in Tuning Application Server Based on Attribute Reduction

Optimizing parameter configuration is an important way to optimize the performance of application server. The optimization strategy based on the traditional parameter adjustment is time-consuming and lacks of systematic and regularity. Based on the modular idea, the model of conditional attribute reduction can be constructed by using the target decision function to classify the parameters of the application server. The application server optimization algorithm based on attribute reduction, can remove parameters which are less important for the target decision function, and obtain the parameters of relative importance, so as to achieve the target focus adjustment, rapidly improve the performance of the system. Based on classical rough set theory, the existing reduction model optimization algorithm is constructed on the basis of equivalence relation, which is easy to cause a lot of damage and loss of information. This article through the expansion of equivalence relation to general two elements, using the generalized rough set theory to improve the application server optimization algorithm of modularization and attribute reduction model based on discernibility function are defined by the reduction of condition attributes, combined with the dependence of the calculation, obtain the final target parameters.