The consensus problem is concerned with the agreement on a system status by the fault-free segment of a processor population in spite of the possible inadvertent or even malicious spread of disinformation by the fault segment of that population. The resulting protocols are useful throughout fault-tolerant distributed systems and will impact the design of other decision systems to come. This paper surveys research on the consensus problem, compares approaches, outlines applications, and suggests directions for future work.

The consensus problem in fault-tolerant computing

A communication architecture for distributed processors is presented here. This architecture is based on a new topolgy we have developed, one which interconnects n nodes by using rn links where the maximum internode distance is logrn, and where each node has, at most, 2r, I/O ports. It is also shown that this network is fault-tolerant, being able to tolerate up to (r − 1) node failures.

A Fault-Tolerant Communication Architecture for Distributed Systems

https://ir.nctu.edu.tw:443/bitstream/11536/16249/1/000304903000006.pdf

The g-good-neighbor conditional diagnosability of hypercube under PMC model

System-level diagnosis appears to be a viable alternative to circuit-level testing in complex multiprocessor systems. A completely new generalization of the characterization problem in the system-level diagnosis area is developed in this paper. This generalized characterization theorem provides necessary and sufficient conditions for any fault-pattern of any size to be uniquely diagnosable, under the symmetric, and asymmetric invalidation models with or without the intermittent faults. Moreover, it is also shown that the well known t-characterization theorems under these models can be derived as special cases. In addition to the generalization provided by these results, it is hoped that these results will also have a great impact on the diagnosis of faulty units in uniform structures based on the system-level diagnosis concepts and would be particularly useful in the diagnosis of WSI-oriented multiprocessor systems.

A Generalized Theory for System Level Diagnosis

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.

/pdf/the-schmi-g-good-neighbor-conditional-diagnosability-of-15um3hpd5g.pdf

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

Large multiprocessor networks require system-level fault diagnosis. Researchers have established and extended a model for such diagnosis.

Friedman

Papers

System-Level Fault Diagnosis