Diagnosability of component-Composition graphs in the mm* model

Chia Wei Lee; Sun-Yuan Hsieh

doi:10.1145/2611759

Diagnosability of component-Composition graphs in the mm* model

Chia Wei Lee, Sun-Yuan Hsieh

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Article number	27
Journal	ACM Transactions on Design Automation of Electronic Systems
Volume	19
Issue number	3
DOIs	https://doi.org/10.1145/2611759
Publication status	Published - 2014 Jan 1

All Science Journal Classification (ASJC) codes

Computer Science Applications
Computer Graphics and Computer-Aided Design
Electrical and Electronic Engineering

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Diagnosability of component-Composition graphs in the mm* model

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