Strong Diagnosability of Matching Composition Networks under Comparison Diagnosis Model

• Main Authors: Yu-Shu Chen, 陳郁樹
• Other Authors: Sun-Yuan Hsieh
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/72012685319307661495

碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 95 === The notion of diagnosability has long played an important role in measuring the reliability of multiprocessor systems. Such a system is t-diagnosable if all faulty nodes can be identified without replacement when the number of faults does not exceed t, where t is some positive integer. Furthermore, a system is strongly t-diagnosable if it is almost (t+1)-diagnosable, except for the case where a node's neighbors are all faulty. In this thesis, we investigate a class of interconnection networks, called Matching Composition Networks (MCNs), and show that they are strongly diagnosable system under the comparison diagnosis model. We also propose some conditions for verifying whether MCNs are strongly diagnosable. Based on our results, we show that, subject to certain conditions, n-diagnosable and n-connected, several well-known interconnection networks are strongly n-diagnosable, including the n-dimensional hypercube, the n-dimensional crossed cube, the n-dimensional Mobius cube, the n-dimensional twisted cube, and the n-dimensional locally twisted cube.