Use of neural networks to recover from software faults in real-time systems

Erwin L. Hunter; Abhijit S. Pandya; Neal Coulter

doi:10.1117/12.170011

2 March 1994 Use of neural networks to recover from software faults in real-time systems

Erwin L. Hunter, Abhijit S. Pandya, Neal Coulter

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.170011
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

In fault-tolerant real-time systems, software errors can be detected, recovered from, and reported using a recovery controller. A simulator has been developed to test the performance of an ANN (Artificial Neural Network) based recovery controller. The simulator for a highly reliable, fault-tolerant multiprocessor telecommunications exchange provides a real-world application to test the effectiveness of the ANN recovery controller. The ANN provides the software recovery controller with the adaptability to determine recovery actions for faults that were previously unseen or not anticipated. This reduces the number of times that human intervention is required to recover the system, and thus reduces the total down-time for the system. The ANN is trained by inserting known faults into the simulated real-time system. The system collects data on the characteristics observed when the fault is detected and uses the back propagation learning algorithm to classify the observed characteristics and recent history of recovery actions into an appropriate recovery action such as restarting or terminating a process, initializing the operating system for a specific processor, or reloading a data base for a processor of group of processors. Once the neural network has been trained, it is used to determine the appropriate responses for faults that occur during the operation of the system.

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Erwin L. Hunter, Abhijit S. Pandya, and Neal Coulter "Use of neural networks to recover from software faults in real-time systems", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.170011

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KEYWORDS

Neurons

Telecommunications

Device simulation

Systems modeling

Computer simulations

Operating systems

Reliability

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