Conditional diagnosability measures for large multiprocessor systems
Diagnosability has played an important role in the reliability of an interconnection network. The classical problem of fault diagnosis is discussed widely and the diagnosability of many well-known networks have been explored. We introduce a new measure of diagnosability, called conditional diagnosab...
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| Veröffentlicht in: | IEEE transactions on computers 2005-02, Vol.54 (2), p.165-175 |
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| creator | Pao-Lien Lai Tan, J.J.M. Chien-Ping Chang Lih-Hsing Hsu |
| description | Diagnosability has played an important role in the reliability of an interconnection network. The classical problem of fault diagnosis is discussed widely and the diagnosability of many well-known networks have been explored. We introduce a new measure of diagnosability, called conditional diagnosability, by restricting that any faulty set cannot contain all the neighbors of any vertex in the graph. Based on this requirement, the conditional diagnosability of the n-dimensional hypercube is shown to be 4(n - 2) +1, which is about four times as large as the classical diagnosability. Besides, we propose some useful conditions for verifying if a system is t-diagnosable and introduce a new concept, called a strongly t-diagnosable system, under the PMC model. Applying these concepts and conditions, we investigate some t-diagnosable networks which are also strongly t-diagnosable. |
| doi_str_mv | 10.1109/TC.2005.19 |
| format | Article |
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The classical problem of fault diagnosis is discussed widely and the diagnosability of many well-known networks have been explored. We introduce a new measure of diagnosability, called conditional diagnosability, by restricting that any faulty set cannot contain all the neighbors of any vertex in the graph. Based on this requirement, the conditional diagnosability of the n-dimensional hypercube is shown to be 4(n - 2) +1, which is about four times as large as the classical diagnosability. Besides, we propose some useful conditions for verifying if a system is t-diagnosable and introduce a new concept, called a strongly t-diagnosable system, under the PMC model. 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The classical problem of fault diagnosis is discussed widely and the diagnosability of many well-known networks have been explored. We introduce a new measure of diagnosability, called conditional diagnosability, by restricting that any faulty set cannot contain all the neighbors of any vertex in the graph. Based on this requirement, the conditional diagnosability of the n-dimensional hypercube is shown to be 4(n - 2) +1, which is about four times as large as the classical diagnosability. Besides, we propose some useful conditions for verifying if a system is t-diagnosable and introduce a new concept, called a strongly t-diagnosable system, under the PMC model. Applying these concepts and conditions, we investigate some t-diagnosable networks which are also strongly t-diagnosable.</description><subject>Computer fault tolerance</subject><subject>conditional diagnosability</subject><subject>conditional faulty set</subject><subject>diagnosability</subject><subject>Fault diagnosis</subject><subject>Graph theory</subject><subject>Hypercubes</subject><subject>Index Terms- PMC model</subject><subject>Multiprocessing</subject><subject>strongly t{\hbox{-}}{\rm diagnosable}</subject><subject>t{\hbox{-}}{\rm diagnosable}</subject><issn>0018-9340</issn><issn>1557-9956</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkD1rwzAQhkVpoenH0rWL6dCh4PQkW5I1FvcTAl3SWcjWOSj4I9XZQ_59HVIodDre4-Hl7mHshsOSczCP63IpAOSSmxO24FLq1BipTtkCgBepyXI4ZxdEWwBQAsyCPZdD78MYht61iQ9u0w_kqtCGcZ906GiKSEkzxKR1cYNJN7Vj2MWhRqJ5SXsasaMrdta4lvD6d16yr9eXdfmerj7fPsqnVVpnYMY0r3RTZ65xukIuC-1BCg8glHDCcJ7lczIovaka3xRSeacVryslJKocvcgu2f2xd77ge0IabReoxrZ1PQ4TWVEAz7lRM3j3D9wOU5xfJFsoLbnk-tD2cITqOBBFbOwuhs7FveVgDzbturQHm5abGb49wgER_8BM69ly9gPp7nBg</recordid><startdate>200502</startdate><enddate>200502</enddate><creator>Pao-Lien Lai</creator><creator>Tan, J.J.M.</creator><creator>Chien-Ping Chang</creator><creator>Lih-Hsing Hsu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Computer fault tolerance conditional diagnosability conditional faulty set diagnosability Fault diagnosis Graph theory Hypercubes Index Terms- PMC model Multiprocessing strongly t{\hbox{-}}{\rm diagnosable} t{\hbox{-}}{\rm diagnosable} |
| title | Conditional diagnosability measures for large multiprocessor systems |
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