欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
系统在遭受入侵或异常攻击的情况下，如何自治地为用户提供非降级服务是网络安全技术中的终极目标．结合鞅差逼近原理，提出了一个基于自律计算的系统服务性能自优化机制（self-optimizationmechanismbasedonautonomiccomputing，SOAC）．该机制能够根据先验自优化知识和内部环境参数信息，以自优化率极小和服务性能极大的要求发掘自优化函数的收敛趋势，实施动态自优化；之后更新最佳自优化模式集，建立并调节预测模型，完成静态自优化，提升自优化预测的精准度．两个层次彼此交互，实现动态变化环境中的系统服务性能自主增长过程．仿真实验结果验证了SOAC机制的有效性与优越性．
英文摘要：
The security of computer and network is a key subject in computer field. Under the intrusion or abnormal attacks, how to supply service autonomously, without being degraded, to users is the ultimate goal of network securiy technology. People need an automated, flexible, fine-grain management method to solve the problem of security decline. Autonomic computing is regarded as a novel method to implement the security self-management of computer and network systems, which has been a frontier research hotspot with the character of subject cross in network security. Combined with the martingale difference principle, a self optimization mechanism based on autonomic computing--SOAC is proposed. According to the prior self optimizing knowledge and parameter information of inner environment, SOAC searches the convergence trend of self optimizing function and executes the dynamic self optimization, aiming at minimizing the optimization mode rate and maximizing the service performance. After that, the best optimization mode set is updated and a prediction model is constructed and renewed, which will implement the static self optimization and improve the accuracy of self optimization prediction. The two procedures interact and cooperate with each other, implementing the autonomic increase of system service performance in the changing inner environment. The simulation results validate the efficiency and superiority of SOAC.
同期刊论文项目
同项目期刊论文
A Novel Problem-solving Metric for Future Internet Routing Based on Virtualization and Cloud-computi
Reputation Revision Method for Selecting Cloud Services Based on Prior Knowledge and a Market Mechan
期刊信息
位置：