Diagnosability Evaluation of the Data Center Network DCell. (14th June 2017) - Li, Xiaoyan; Fan, Jianxi; Lin, Cheng-Kuan; Jia, Xiaohua
Abstract: With the rapid development of cloud computing, many large-scale data centers are being built to provide increasingly popular online application services. This leads to the proposal of data center networks (DCNs) supporting millions of servers with high-network capacity by using only commodity switches. The k -dimensional DCell with n -port switches and t k, n servers, D k, n, has been proposed as a model for a large-scale DCN with a server-centric structure. In this paper, we study the diagnosability and the g -good-neighbor conditional diagnosability of D k, n . We prove that: (i) D k, n is ( n + k − 1 ) -diagnosable under the precise diagnosis strategy and ( 2 k + n − 2 ) / ( 2 k + n − 2 ) - diagnosable under the pessimistic diagnosis strategy; (ii) the g -good-neighbor conditional diagnosabilities of D k, n under the PMC model and the MM * model are both ( g + 1 ) k + n − 1 (resp. ( n + k − g ) t g − n + 1, n − 1 ) with 0 ≤ g ≤ n − 1 (resp. n ≤ g ≤ n + k − 2 ), which is almost ( g + 1 ) (resp. t g − n + 1, n ) times of the traditional diagnosability. These results provide a quantitative evaluation for a large-scale DCN's reliability and availability.