Data center networks (DCNs) always strive to ensure high reliability and fault tolerance when Big Data processing and cloud computing are carried out. One effective method is to conduct the fault diagnosis based on subsystem structures (establish the subsystem-based reliability), which can be transformed into solving the problem of the probability that there exists at least one fault-free subnetwork in one network. In this article, we establish the subsystem-based reliability of BCube, which is the first time that fault diagnosis is carried out in the subsystem structures of the DCN. More specifically, we compute the upper and lower bounds on the subsystem-based reliability of BCube and determine the approximation on the subsystem-based reliability of BCube. Furthermore, we conduct some numerical simulations to validate the established analytical formulation. Our results show that the precise value of subsystem-based reliability of BCube can be basically represented by the approximation value of subsystem-based reliability of BCube, which means that it is much easier to evaluate the reliability of the network even when the network scale is sufficient large. Although the analysis is done for a particular network (BCube), the outcome can serve as a useful reference, and can shed light on the effectiveness of the fault diagnosis for other DCNs.

Network virtualization (NV) has extensive and significant applications in cloud computing and parallel and distributed systems. Virtual network embedding (VNE) is a key issue in NV, which is an effective means to advance systems’ performance. While existing VNE research lacks resource allocation coordination between mappings of different virtual network requests, resulting in insufficient resource utilization and high overhead. In this article, we propose a novel node essentiality evaluation model for data center networks (DCNs), and design an efficient distributed collaborative virtual network embedding. Firstly, we propose a node essentiality evaluation scheme based on dynamic model, which combines the characteristics of network topology and nodes to make the evaluation results more comprehensive. Secondly, we establish the two-stage node importance evaluation criteria for the deviation mean of the data center dynamic model and the variance based on the deviation mean. Furthermore, we investigate a nodal importance assessment method based on the data center dynamic model for perturbation testing. Finally, we design a distributed coordinated VNE algorithm ( CNI-VNE ) which calculates the importance index of physical nodes through topology awareness. The proposed algorithm can increase the coordination between different request mappings, thereby reducing the mapping cost of physical node resources and minimizing the cost of VNE. We use the real Fat-tree DCN of 128 servers and 80 switches as testbed, and evaluate them from indicators such as average reliability, average bandwidth consumption, average energy consumption, and average mapping time. Massive simulation results in different scenarios show that our algorithm achieves the best performance on most indicators compared with the existing state-of-the-art proposals, mapping acceptance and average revenue increased by 19.4% and 21.3%, respectively, and DCN reduced bandwidth consumption by about 30%.

With the increasing demand of data center interconnection in the network cloud era, data center networks (DCNs) play key roles in computing and communication, becoming the center of a great deal of resources and business. Therefore, how to design a DCN with good performance has been a significant issue in networks. The hypercube is a popular interconnection network topology with low diameter, symmetry and recursive structure, and scalability. As a variant of the hypercube, the crossed cube not only retains the excellent properties of the hypercube, but also performs better in terms of Hamiltonian-connectivity, embeddability, diameter, etc. In this paper, we first propose a novel and server-centric DCN, called CSDC, which is based on the crossed cube network. The performance of CSDC can be characterized by the properties of its logical structure, so we then give its logical structure $C_{n}$ , and determine the connectivity and edge-connectivity of $C_{n}$ . Furthermore, we explore the fault-tolerant paths and disjoint paths between any two distinct nodes in $C_{n}$ , and obtain the diameter of $C_{n}$ . Moreover, we propose a method to construct two completely independent spanning trees (CISTs) in $C_{n}$ . Extensive evaluations demonstrate that CSDC is a magnetic DCN for building large-scale data centers.

3D tori are significant interconnection architectures in building supercomputers and parallel computing systems. Due to the rapid growth of edge faults and the crucial role of path structures in large-scale distributed systems, fault-tolerant path embedding and correlated issues have drawn widespread researches. However, existing path embedding methods are based on traditional fault models, allowing all faults to be near the same node, so they usually only focus on theoretical proof and generate linear fault-tolerance related to dimension n . In order to improve the fault-tolerance of 3D torus, we first propose a novel conditional fault model called the Locally Faulty Block model (LFB model) . On the basis of this model, the Hamiltonian paths with large-scale edge defects in torus are investigated. After that, we construct an Hamiltonian path embedding algorithm HP-LFB into torus with O ( N ) under the LFB model, where N is the number of nodes in torus. Furthermore, we present an adaptive routing algorithm HoeFA, which is based on the method of distance vector to limit the use of virtual channels (VCs). We also make a comparison with state-of-the-art schemes, indicating that our scheme enhance other comprehensive results. The experiment indicated that HP-LFB can sustain the dynamic degradation of the batting average of establishing Hamiltonian paths, with the added faulty edges exceeding fault-tolerance.

Multi-path transmission enables load balancing and improves network performance in data center networks (DCNs). It increases the possibility of network congestion and makes traditional network traffic engineering methods inefficient due to the uneven distribution of network traffic in data centers. In this paper, we present a reliable and efficient Disjoint paths based Multi-Path Transmission scheme (DMPT) that selects distributed requests through topology awareness. Firstly, we propose disjoint path construction algorithms through rigorous theoretical proof, aiming at the different transmission requirements of DCNs. Secondly, we offer an optimal solution to the disjoint multi-path selection problem, which is aimed at the trade-off between link load and transmission time. Furthermore, DMPT can split the flow over multiple transmission paths based on the link status. Finally, extensive experiments are executed for DMPT on a novel EHDC of DCN that is based on exchanged hypercube. The experimental results show that DMPT can reduce the average running time by 18.6%, and the average path length is close to the optimal path. Furthermore, it achieves significant improvements in balancing network link traffic and facilitating deployment, which also reflects the advantages of topology aware multiplexing in practice.