A Tree Structure for Local Diagnosis in Multiprocessor Systems Under Comparison Model

Meirun Chen, Cheng Kuan Lin*, Kung Jui Pai

*此作品的通信作者

研究成果: Conference contribution同行評審

摘要

If we only care about the status of a particular vertex, instead of doing global diagnosis, Hsu and Tan introduced the concept of local diagnosis and proposed an extended star structure to diagnose a vertex under comparison model. Usually, there is a gap between the local diagnosability and the lower bound guaranteed by the extended star structure mentioned above. In this paper, we propose a new testing structure and corresponding diagnosis algorithm to diagnose a vertex under comparison model to better evaluate the local diagnosability. The local diagnosability of a vertex is upper bounded by its degree in the system. If the local diagnosability of each vertex equals to its degree in the system then we say this system has the strong local diagnosability property. Based on the new structure, we show that the n-dimensional star graph Sn with faulty links has the strong local diagnosability property provided that each vertex connects to at least three fault-free links. Simulations are presented to show the performance of our tree structure.

原文English
主出版物標題New Trends in Computer Technologies and Applications - 25th International Computer Symposium, ICS 2022, Proceedings
編輯Sun-Yuan Hsieh, Ling-Ju Hung, Sheng-Lung Peng, Ralf Klasing, Chia-Wei Lee
發行者Springer Science and Business Media Deutschland GmbH
頁面49-60
頁數12
ISBN(列印)9789811995811
DOIs
出版狀態Published - 2022
事件25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022 - Taoyuan, 台灣
持續時間: 15 12月 202217 12月 2022

出版系列

名字Communications in Computer and Information Science
1723 CCIS
ISSN(列印)1865-0929
ISSN(電子)1865-0937

Conference

Conference25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022
國家/地區台灣
城市Taoyuan
期間15/12/2217/12/22

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