A t/k diagnosis algorithm on hypercube-like networks

Abstract

Processor fault diagnosis takes a key role in fault-tolerant computing on multiprocessor systems. The t/k diagnosis strategy which is a generalization of the precise and pessimistic diagnosis strategies can significantly improve the self-diagnosing capability of the system. Using this tool, it is possible to deal with large faults in the system. This paper presents a t/k diagnosis algorithm on n-dimensional hypercube-like networks (include Hypercubes, Crossed cubes, Möbius cubes, Locally Twisted cubes, and Twisted cubes) for any k∈[0,n−2]. The algorithm can correctly identify all nodes except at most k nodes undiagnosed. It runs in O(N) time, where N=2n is the total number of nodes of n-dimensional hypercube-like networks. To the best of our knowledge, in the case k≥4, there is no known t/k diagnosis algorithm for general diagnosable system or any specific system.