Self-Optimization Method of System Service Dependability Based on Autonomic Computing

ZHU Li-na; WU Qing-tao; LOU Ying; ZHENG Rui-juan

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Self-Optimization Method of System Service Dependability Based on Autonomic Computing

更新时间：2026-09-04

- Self-Optimization Method of System Service Dependability Based on Autonomic Computing
- Vol. 30, Issue 8, Pages: 63-66(2013)
- 作者机构：
  
  河南科技大学机构电子信息工程学院,河南,洛阳,471023
- 作者简介：
- 基金信息：
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- Published：2013
- 稿件说明：
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朱丽娜, 吴庆涛, 娄颖, 郑瑞娟. 基于自律计算的系统服务可信性自优化方法[J]. 微电子学与计算机, 2013, 30(8): 63-66.

ZHU Li-na, WU Qing-tao, LOU Ying, et al. Self-Optimization Method of System Service Dependability Based on Autonomic Computing[J]. 2013, 30(8): 63-66.
朱丽娜, 吴庆涛, 娄颖, 郑瑞娟. 基于自律计算的系统服务可信性自优化方法[J]. 微电子学与计算机, 2013, 30(8): 63-66. DOI：

ZHU Li-na, WU Qing-tao, LOU Ying, et al. Self-Optimization Method of System Service Dependability Based on Autonomic Computing[J]. 2013, 30(8): 63-66. DOI：

摘要

提出了一种基于 Q 学习的系统服务性能自优化方法。该方法通过感知网络系统的服务性能状态参数变化，利用前馈神经网络的非线性映射关系得到执行动作，综合系统服务性能变化情况及服务的可用性来计算环境奖赏函数值，利用 Q 学习的自学习特性和预测能力，使系统服务性能达到最优。仿真结果表明，该方法对优化系统整体可信性和服务效用具有明显的优越性。

Abstract

According to the notion of autonomic computing

a network system service performance online real-time optimization method is proposed based on Q-learning algorithm. First

service performances are taken as parameters

which can affect network system as targets.Second

feed forward neural network is used as nonlinear mapping to gain executive action.Finally

the environment reward function values are calculated according to the change of system service performance and service availability.Then self learning feature and predictive ability of Q-learning is used to make the system service performance achieve optimization.The simulation results show that the optimization method has obvious advantages in credibility and service utility of the overall system maintenance.

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