Diagnosability of Component-Composition Graphs in the MM* Model
Chia‐Wei Lee, Sun‐Yuan Hsieh · ACM Transactions on Design Automation of Electronic Systems · 2014
Diagnosability is an important metric for measuring the reliability of multiprocessor systems. This article adopts the MM* model and outlines the common properties of a wide class of interconnection networks, called component-composition graphs (CCGs), to determine their diagnosability by using their obtained properties. By applying the results to multiprocessor systems, the diagnosability of hypercube-like networks (including hypercubes, crossed cubes, Möbius cubes, twisted cubes, locally twisted cubes, generalized twisted cubes, and recursive circulants), star graphs, pancake graphs, bubble-sort graphs, and burnt pancake graphs, all of which belong to the class of CCGs, can also be computed.