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杨启亮, 吕建, 陶先平, 马晓星, 邢建春, 宋巍. 不确定条件下任务关键软件的模糊自适应[J]. 计算机科学技术学报, 2013, 28(1): 165-187. DOI: 10.1007/s11390-013-1321-9
引用本文: 杨启亮, 吕建, 陶先平, 马晓星, 邢建春, 宋巍. 不确定条件下任务关键软件的模糊自适应[J]. 计算机科学技术学报, 2013, 28(1): 165-187. DOI: 10.1007/s11390-013-1321-9
Qi-Liang Yang, Jian Lv, Xian-Ping Tao, Xiao-Xing Ma, Jian-Chun Xing, Wei Song. Fuzzy Self-Adaptation of Mission-Critical Software Under Uncertainty[J]. Journal of Computer Science and Technology, 2013, 28(1): 165-187. DOI: 10.1007/s11390-013-1321-9
Citation: Qi-Liang Yang, Jian Lv, Xian-Ping Tao, Xiao-Xing Ma, Jian-Chun Xing, Wei Song. Fuzzy Self-Adaptation of Mission-Critical Software Under Uncertainty[J]. Journal of Computer Science and Technology, 2013, 28(1): 165-187. DOI: 10.1007/s11390-013-1321-9

不确定条件下任务关键软件的模糊自适应

Fuzzy Self-Adaptation of Mission-Critical Software Under Uncertainty

  • 摘要: 任务关键软件必须提供连续、在线的服务以确保关键任务的成功执行.自适应是确保不确定条件下任务关键软件服务质量和可用性所必需的一种能力.现今,鲜有技术来处理任务关键软件的自适应问题,而且现有绝大多数软件自适应方法也没有考虑自适应环中的不确定性问题.为了解决上述问题,我们提出了一种基于模糊控制的方法,即软件模糊自适应,以应对不确定条件下任务关键软件的自适应这一研究挑战.首先,我们提出了软件模糊自适应的概念框架,该框架分为感知、决策和动作三个阶段;同时建立了软件模糊自适应的形式模型,从而为我们的方法奠定了严格的数学基础.其次,我们开发了一种新颖的软件模糊自适应实现技术及其支撑工具,即SFSA工具包,来自动化软件模糊自适应的实现过程.最后,我们通过开发一个过程控制系统中的自适应任务关键软件来展示我们方法的有效性.验证实验表明,本文提出的基于模糊控制的方法是有效的且具有低的系统开销.

     

    Abstract: Mission-critical software (MCS) must provide continuous, online services to ensure the successful accomplishment of critical missions. Self-adaptation is particularly desirable for assuring the quality of service (QoS) and availability of MCS under uncertainty. Few techniques have insofar addressed the issue of MCS self-adaptation, and most existing approaches to software self-adaptation fail to take into account uncertainty in the self-adaptation loop. To tackle this problem, we propose a fuzzy control based approach, i.e., Software Fuzzy Self-Adaptation (SFSA), with a view to deal with the challenge of MCS self-adaptation under uncertainty. First, we present the SFSA conceptual framework, consisting of sensing, deciding and acting stages, and establish the formal model of SFSA to lay a rigorous and mathematical foundation of our approach. Second, we develop a novel SFSA implementation technology as well as its supporting tool, i.e., the SFSA toolkit, to automate the realization process of SFSA. Finally, we demonstrate the effectiveness of our approach through the development of an adaptive MCS application in process control systems. Validation experiments show that the fuzzy control based approach proposed in this work is effective and with low overheads.

     

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