Fault Propagation Reasoning and Diagnosis for Computer Networks Using Cyclic Temporal Constraint Network Model

作者：Cui, Yiqian^*; Shi, Junyou; Wang, Zili

来源：IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2017, 47(8): 1965-1978.

DOI：10.1109/TSMC.2016.2583509

• Binary optimization
• computer networks
• temporal constraint network (TCN)
• temporal reasoning
• time distance

Fault diagnosis, including fault detection and isolation, is a critical task for computer networks. Among the various techniques used for online system-level diagnosis, we are interested in the approach based on temporal information processing. The delays of the computer networks are inevitable, and the fault localization process has to take into account bounded delays or the temporal constraints. Temporal information is fundamental in model-based diagnosis. There can be cycles or loops in a computer network, but the fault reasoning methods for such cases are seldom considered in the literature. This paper provides an analytic model based on the cyclic temporal constraint network (CTCN), which aims at the fault diagnosis of cyclic computer networks using temporal information. The goal of the proposed framework is twofold: given the network structures and the predetermined candidate fault causes, the CTCN model corresponding the computer network under test is formulated; based on the CTCN model, given the alarms sequences with timestamps, the fault diagnosis process is executed to determine the most likely fault cause(s) with its/their time interval(s) of occurrence(s). The reasoning method is dependent on time point and time distance information, with which the fault motivators (i. e., actors) and fault responders (i. e., victims) can be identified. The calculation process consists of three steps: 1) establishment of the objective function; 2) determination of the fault propagation paths; and 3) determination of the expected states with a given fault hypothesis. Finally, the proposed method is demonstrated via an application study, and the effectiveness of our proposed method is verified.

• 出版日期2017-8
• 单位北京航空航天大学