摘要
将神经网络应用于计算系统的抗衰重启技术中,以实现细粒度的软件抗衰,可以更大程度地增强软件抗衰的智能化,提高抗衰效率及准确性,进一步降低抗衰开销,提高软件可靠性.判定模块重启相关性及模块可达集是实施细粒度软件抗衰策略的关键环节.文中结合神经网络工作原理,构建了判定模块间重启相关度及模块可达集的神经网络结构模型.该模型根据软件系统中模块间的控制、调用及数据访问关系,通过分析模块间的耦合程度和重启相关性的相关理论及其之间的关系,制定模块重启相关度和模块可达集的判定算法,最终完成系统模块间重启相关度及模块可达集的判定任务,从而为实现智能化细粒度软件抗衰提供支持.
In order to improve the software availability and reliability, intelligentize the software rejuvenation and boost up its veracity and efficiency, the rejuvenation granularity would be finer and artificial neural network would be applied. The key step of fine-grained software rejuvenation is to determine restart dependence between the modules. This paper researches the principium of artificial neural network, puts forward the model of artificial neural network which determines the degree of restart dependence between modules and reachable set of each module finally. Based on the coupling relation between modules of software system, this model analyzes the connection between restart dependence and coupling relation, sets down the arithmetic to calculate the degree of restart dependence between modules and reachable set of each module; so that the intelligent software rejuvenation with fine rejuvenation granularity is supported.
出处
《计算机学报》
EI
CSCD
北大核心
2008年第7期1268-1275,共8页
Chinese Journal of Computers
基金
国家自然科学基金项目(60273035)资助
关键词
软件抗衰
抗衰粒度
重启相关度
神经网络
software rejuvenation
rejuvenation granularity
degree of restart dependence
artificial neural network
