Reliability evaluation of complete graph-based recursive networks

With the development of cloud computing and high-performance computing technologies, the scales of data center networks and interconnection networks become more and more large. As a result, the reliability evaluation of the two kinds of networks is very important. And most scholars study the reliability of specific networks. In this paper, we put forward a kind of complete graph-based recursive networks (short as CGRNs) which includes data center networks—DCell and generalized DCell, as well as the interconnection network dragonfly, etc. Then we further investigate the reliability of such networks—the connectivity, the diagnosability under the pessimistic diagnosis strategy based on the PMC model, g-restrict connectivity, and the g-good-neighbor conditional diagnosabilities under the PMC model and the MM⁎ model. As applications, previous results of DCell networks can be directly obtained and new results on the connectivity, diagnosability under the pessimistic strategy diagnosis based on the PMC model, g-restrict connectivity, and the g-good-neighbor conditional diagnosabilities under the PMC model and the MM⁎ model of networks (such as generalized DCell networks and dragonfly networks), are derived. Even these reliability results can be obtained for some other networks different from DCell networks, generalized DCell networks, and dragonfly networks. In addition, it can be seen that the diagnosability under the pessimistic diagnosis strategy based on the PMC model, the g-restrict connectivity, and the g-good-neighbor conditional diagnosabilities under the PMC model and the MM⁎ model of Gr are about 2 times of its diagnosability, g+1 times of its connectivity, and g+1 times of its diagnosability, respectively.