›› 2013, Vol. 28 ›› Issue (1): 165-187.doi: 10.1007/s11390-013-1321-9

Special Issue: Software Systems

• Software Engineering • Previous Articles     Next Articles

Fuzzy Self-Adaptation of Mission-Critical Software Under Uncertainty

Qi-Liang Yang1,2 (杨启亮), Member, CCF, IEEE, Jian Lv1 (吕建), Fellow, CCF, Member, ACM Xian-Ping Tao1 (陶先平), Member, CCF, IEEE, Xiao-Xing Ma1(马晓星), Member, CCF, IEEE Jian-Chun Xing2 (邢建春), Member, CCF, IEEE, and Wei Song1,3 (宋巍), Member, CCF, ACM, IEEE   

  1. 1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China;
    2. School of National Defense Engineering, PLA University of Science and Technology, Nanjing 210007, China;
    3. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China
  • Received:2012-03-20 Revised:2012-09-29 Online:2013-01-05 Published:2013-01-05
  • Supported by:

    Supported by the National Natural Science Foundation of China under Grant Nos. 60736015, 61073031, 60973044, 61003019, and the National Basic Research 973 Program of China under Grant No. 2009CB320702.

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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