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浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (2): 213-219    DOI: 10.3785/j.issn.1008-973X.2010.02.001
计算机技术﹑电信技术     
基于审查不确定性的预见式软件自适应
王华1,2, 应晶1, 蒋涛1
(1. 浙江大学 计算机科学与技术学院,浙江 杭州 310027；2. 杭州电子科技大学 管理科学与信息工程研究所,浙江 杭州 310018)
Proactive self-adaptation of software based on inspecting uncertainty
WANG Hua1,2, YING Jing1, JIANG Tao1
(1.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;
2. Institute of Management Science and Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China)
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摘要：

为了解决传统的被动式自适应（RSA）模型自适应不可预期的问题,提出预见式的自适应（PSA）方法.PSA方法要解决的重要问题是：模型如何能够从环境中学习.通过使用隐Markov模型（HMM）,系统能够从历史行为中进行学习并生成预见式的动作.和传统的RSA模型相比,PSA方法能预见式地调整系统的运行时行为以适应新的环境.通过对系统管理的观察和PSA方法的认知能力,应用系统能够做出合理的决策.应用程序能实现预见性的自主管理过程,减少了人工干预.实验结果表明,PSA方法为应用提供了预见式的自适应管理机制,提高了应用的可管理性和服务质量(QoS).
Abstract:

A method of proactive self-adaptation (PSA) was proposed to address the unanticipated adaptation of the traditional reactive self-adaptation (RSA) model. The PSAmethod presented an important problem to be resolved how the model learns from the environment. Hidden Markov model (HMM) was employed to learn from history behavior of targetsystem, and then generated anticipatory actions. The PSA method can proactively adjust the runtime behaviors of the system to be adaptive to the new situations  compared to thetraditional RSA model. The application system made sound decision by combining the observation from system administrators and the cognitive power of PSA. Then applicationsimplemented the proactive autonomic management and reduced manual operation. Experimental results show that the PSA method provides for application with proactive self-adaptivemanagement mechanism and improves the manageability and quality of service (QoS) of application.
出版日期: 2010-03-09
:  TP 311.5  
基金资助:

国家“863”高技术研究发展计划资助项目（2007AA01Z187）.
通讯作者: 应晶,男,教授.     E-mail: yingj@zucc.edu.cn
作者简介: 王华（1978－）,男,浙江金华人,博士生,从事自适应软件和软件体系结构的研究.
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引用本文:

王华, 应晶, 蒋涛. 基于审查不确定性的预见式软件自适应[J]. J4, 2010, 44(2): 213-219.

WANG Hua, YING Jing, JIANG Chao. Proactive self-adaptation of software based on inspecting uncertainty. J4, 2010, 44(2): 213-219.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2010.02.001    或    http://www.zjujournals.com/eng/CN/Y2010/V44/I2/213

［1］ GABRIEL R P, GOLDMAN R. Conscientious software ［C］// Proceedings of the 21st Annual ACM SIGPLAN Conference on Object-oriented Programming Systems, Languages, and

Applications. Portland. Oregon, USA: ACM, 2006: 433-450.
［2］ SECELEANU T, GARLAN D. Developing adaptive systems with synchronized architectures ［J］. Journal of Systems and Software, 2006, 79(11): 1514-1526.
［3］ GARLAN D, CHENG S W, HUANG A C, et al. Rainbow: architecture-based self-adaptation with reusable infrastructure ［J］. Computer, 2004, 37(10): 46-54.
［4］ CHENG Shang-wen, GARLAN D, SCHMERL B. Architecture-based self-adaptation in the presence of multiple objectives ［C］// Proceedings of the ICSE 2006 Workshop on Software

Engineering for Adaptive and Self-Managing Systems (SEAMS). Shanghai, China: ACM, 2006: 2-8.
［5］ CHENG S W, GARLAN D. Handling uncertainty in autonomic systems ［C］// International Workshop on Living with Uncertainties (IWLU′07), Colocated with the 22nd

International Conference on Automated Software Engineering (ASE′07). Atlanta, Georgia, USA: ［s. n.］, 2007.
［6］ POLADIAN V, GARLAN D, SHAW M, et al. Leveraging resource prediction for anticipatory dynamic configuration ［C］// Self-Adaptive and Self-Organizing Systems, 2007. SASO′

07. First International Conference on. Boston, Massachusetts, USA: IEEE, 2007: 214-223.
［7］ CELIKU O, GARLAN D, SCHMERL B. Augmenting architectural modeling to cope with uncertainty ［C］// Proceedings of the International Workshop on Living with Uncertainty(IWLU

′07). Atlanta, Georgia, USA: ［s. n.］, 2007.
［8］ DAI Y S, XIE M, LONG Q, et al. Uncertainty analysis in software reliability modeling by Bayesian analysis with maximum-entropy principle ［J］. Software Engineering, IEEE

Transactions on, 2007, 33(11): 781-795.
［9］ CHEUNG L, GOLUBCHIK L, MEDVIDOVIC N, et al. Identifying and addressing uncertainty in architecture-level software reliability modeling ［C］// Parallel and Distributed

Processing Symposium, 2007. IPDPS 2007. IEEE International. Long Beach, California, USA: IEEE, 2007: 1-6.
［10］ 王实, 高文.基于隐马尔可夫模型的在线零售站点的自适应 ［J］. 软件学报, 2001, 12(4): 599-606.
WANG Shi, GAO Wen. Adaptive online retail Web site based on hidden Markov model［J］. Journal of Software, 2001, 12(4): 599-606.
［11］ RABINER L R. A tutorial on hidden Markov models and selected applications in speech recognition ［J］. Proceedings of the IEEE, 1989, 77(2): 257-286.
［12］ JAVIER C, CARLOS C, JAVIER C, et al. An aspect-oriented adaptation framework for dynamic component evolution ［J］. Electronic Notes in Theoretical Computer Science,

2007, 189(1): 21-34.
［13］ POLADIAN V, SOUSA J P, GARLAN D, et al. Dynamic configuration of resource-aware services ［C］// Proceedings of the 26th International Conference on Software Engineering.

Edinburgh, United Kingdom: Institute of Electrical and Electronics Engineers Computer Society, Piscataway, United States, 2004: 604-613.
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