The ${\schmi g}$-Good-Neighbor Conditional Diagnosability of ${\schmi k}$-Ary ${\schmi n}$-Cubes under the PMC Modeland MM* Model

Abstract

The diagnosability of a system is defined as the maximum number of faulty processors that the system can guarantee to identify, which plays an important role in measuring of the reliability of multiprocessor systems. In the work of Peng et al. in 2012, they proposed a new measure for fault diagnosis of systems, namely, $g$ -good-neighbor conditional diagnosability. It is defined as the diagnosability of a multiprocessor system under the assumption that every fault-free node contains at least $g$ fault-free neighbors, which can measure the reliability of interconnection networks in heterogeneous environments more accurately than traditional diagnosability. The $k$ -ary $n$ -cube is a family of popular networks. In this study, we first investigate and determine the $R_g$ -connectivity of $k$ -ary $n$ -cube for $0\le g\le n.$ Based on this, we determine the $g$ -good-neighbor conditional diagnosability of $k$ -ary $n$ -cube under the PMC model and MM* model for $k\ge 4, n\ge 3$ and $0\le g\le n.$ Our study shows the $g$ -good-neighbor conditional diagnosability of $k$ -ary $n$ -cube is several times larger than the classical diagnosability of $k$ -ary $n$ -cube.