Conditional diagnosability of component-composition graphs under the PMC model

Abstract

Diagnosability is an important metric for measuring the reliability of multiprocessor systems. The conditional diagnosability, which is more general than the classical diagnosability, is to measure the diagnosability of a multiprocessor system under the assumption that all of the neighbors of any node in the system cannot fail at the same time. This paper adopts the PMC model and outlines the common properties of a wide class of interconnection networks, called component-composition graphs (CCGs), to determine their conditional diagnosability by using the derived properties. As applications, the diagnosability of hypercube-like networks (including hypercubes, crossed cubes, Möbius cubes, twisted cubes, locally twisted cubes, generalized twisted cubes, and recursive circulants), and bubble-sort graphs, all of which belong to CCGs can be readily obtained.