The demand for data center bandwidth has exploded due to the continuous development of cloud computing, causing the use of network resources close to saturation. Optical network has become an encouraging technology for many burgeoning networks and parallel/distributed computing applications because of its huge bandwidth. This article focuses on efficient embedding of data centers into optical networks, which aims to reduce complexity of the network topology by using the parallel transmission characteristics of optical fiber. We first present a novel virtual network embedding (VNE) mathematical model used for optical data center networks. Then we derive a priority of location VNE algorithm according to node proximity sensing and path comprehensive evaluation. Furthermore, we propose routing and wavelength assignment for DCNs into optical networks, and identify the lower bound of the required number of wavelengths. Extensive evaluations show that the proposed embedding algorithm can reduce the average waiting time of virtual network requests by 20 percent, increase the request acceptance rate and revenue-overhead ratio by 13 percent, as compared to the latest VNE algorithm.

BCube is a promising structure of data center network, as it can significantly improve the performance of typical applications. With the expansion of network scale and increasement of complexity, reliability and stability of networks have become more essential. In this paper, we study the fault-tolerant routings in BCube. First, we design a fault-tolerant routing algorithm based on node disjoint multi-paths. The proposed multi-path routing has stronger fault tolerance, since each path has no other common nodes except the source node and the destination node. Second, we investigate an effective fault-tolerant routing based on routing capabilities algorithm for BCube. The proposed algorithm has higher fault tolerance and success rate of finding feasible routes, since it does not limit the faults number. Third, we present an adaptive path finding algorithm for establishing virtual links between any two nodes in BCube, which can shorten the diameter of BCube. Extensive simulation results show that the proposed routing scheme outperforms the existing popular algorithms. Compared with the state-of-the-art fault-tolerant routing algorithms, the proposed algorithm has a 21.5% to 25.3% improvement on both throughput and packet arrival rate. Meanwhile, it reduces the average latency of 18.6% and the maximum latency of 23.7% in networks.

The explosive growth of users and applications in IoT environments has promoted the development of cloud computing. In the cloud computing environment, task scheduling plays a crucial role in optimizing resource utilization and improving overall performance. However, effective task scheduling remains a key challenge. Traditional task scheduling algorithms often rely on static heuristics or manual configuration, limiting their adaptability and efficiency. To overcome these limitations, there is increasing interest in applying reinforcement learning techniques for dynamic and intelligent task scheduling in cloud computing. How can reinforcement learning be applied to task scheduling in cloud computing? What are the benefits of using reinforcement learning-based methods compared to traditional scheduling mechanisms? How does reinforcement learning optimize resource allocation and improve overall efficiency? Addressing these questions, in this paper, we propose a Q-learning-based Multi-Task Scheduling Framework (QMTSF). This framework consists of two stages: First, tasks are dynamically allocated to suitable servers in the cloud environment based on the type of servers. Second, an improved Q-learning algorithm called UCB-based Q-Reinforcement Learning (UQRL) is used on each server to assign tasks to a Virtual Machine (VM). The agent makes intelligent decisions based on past experiences and interactions with the environment. In addition, the agent learns from rewards and punishments to formulate the optimal task allocation strategy and schedule tasks on different VMs. The goal is to minimize the total makespan and average processing time of tasks while ensuring task deadlines. We conducted simulation experiments to evaluate the performance of the proposed mechanism compared to traditional scheduling methods such as Particle Swarm Optimization (PSO), random, and Round-Robin (RR). The experimental results demonstrate that the proposed QMTSF scheduling framework outperforms other scheduling mechanisms in terms of the makespan and average task processing time.

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.