摘要
针对异构网格环境下的依赖任务调度问题面临的安全性挑战,综合考虑网格资源节点的固有安全性和行为安全性,构建一个网格资源节点身份可靠性度量函数和行为表现信誉度评估策略;同时为了确立任务安全需求与资源节点安全属性之间的隶属关系,定义了安全效益隶属度函数,从而建立了一个网格任务调度的安全可信模型。以此为基础,定义任务需求表示模型和网格资源拓扑模型,提出一种安全可信的网格任务调度新模型。为求解该模型,在遗传算法的基础上,设计新的进化算子即改进的交叉算子、内部交叉算子及作为变异的迁移算子,同时引入模拟退火算法增加搜索精度,从而提出了一种新的遗传-退火算法。仿真实验表明,在相同条件下,该算法比同类算法在调度长度、安全可信值及收敛性等方面具有更好的综合性能。
Given the security challenges faced for the dependent task-scheduling problem under heterogeneous grid envi- ronment, considering grid nodes＇ inherent security and behavioral security, we built a function to measure each node＇s identical reliability and a strategy to assess its behavioral credibility. Meanwhile, in order to establish the affiliation be- tween the security requirement and security attributes of each task, an affiliation function of security benefits was de- fined. Thus, a security trusted task-scheduling model under grid environment was built in this paper. On this hasis, with the task＇s requirement model presented and grid resources~ topology model introduced, a new security trusted grid task-scheduling model was proposed. To solve this model by using genetic algorithm, we designed several new genetic operators, including improved crossover operator, crossover operator within each individual and migration operator which is taken as mutation operator, and at the same time, with a view to increase search precision, the simulated annea- ling algorithm was introduced, by which we further proposed a new genetic-annealing algorithm. The simulation results show that compared with similar algorithms under the same conditions, the proposed algorithm has a better overall per- formance in terms of scheduling length, security trusted value, convergence and other aspects.
出处
《计算机科学》
CSCD
北大核心
2015年第6期268-275,共8页
Computer Science
基金
国家自然科学基金资助项目(61103143)
中国博士后科学基金资助项目(2012M512008)
河南省科技厅科技发展计划重点科技攻关项目(142102110152)
河南省高校科技创新人才支持计划项目(2012HASTIT032)
河南省教育厅科学技术研究重点项目指导计划基础前沿项目(14B520057)资助
关键词
网格计算
任务调度
安全可信模型
进化算子
遗传-退火算法
Grid computing
Task-scheduling
Security trusted model
Evolution operator
Genetic-annealing algorithm
