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• W3026569222 abstract "Diagnosability is an important metric for measuring the reliability of multiprocessor systems. The conditional diagnosability, which is more general than the classical diagnosability, is to measure the diagnosability of a multiprocessor system under the assumption that all of the neighbors of any node in the system cannot fail at the same time. This paper adopts the PMC model and outlines the common properties of a wide class of interconnection networks, called component-composition graphs (CCGs), to determine their conditional diagnosability by using the derived properties. As applications, the diagnosability of hypercube-like networks (including hypercubes, crossed cubes, Möbius cubes, twisted cubes, locally twisted cubes, generalized twisted cubes, and recursive circulants), and bubble-sort graphs, all of which belong to CCGs can be readily obtained." @default.
• W3026569222 created "2020-05-29" @default.
• W3026569222 creator A5015598447 @default.
• W3026569222 date "2020-10-01" @default.
• W3026569222 modified "2023-09-26" @default.
• W3026569222 title "Conditional diagnosability of component-composition graphs under the PMC model" @default.
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• W3026569222 doi "https://doi.org/10.1016/j.tcs.2020.05.013" @default.
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