Connecting a large number of servers with high bandwidth links is one of the most crucial and challenging tasks that the Data Center Network (DCN) must fulfill. DCN faces a lot of difficulties like the effective exploitation of DC components that, if highlighted, can aid in constructing high performance, scalable, reliable, and cost-effective DCN. In this paper, we investigate the server-centric structure. We observe that current DCs use servers that mostly come with dual ports. Effective exploitation of the ports of interest for building the topology structure can help in realizing the potentialities of reducing expensive topology. Our new network topology, named “Parallel Cubes” (PCube), is a duplicate defined structure that utilizes the ports in the servers and mini-switches to form a highly effective, scalable, and efficient network structure. P-Cube provides high performance in network latency and throughput and fault tolerance. Additionally, P-Cube is highly scalable to encompass hundreds of thousands of servers with a low stable diameter and high bisection width. We design a routing algorithm for P-Cube network that utilizes the P-Cube structure to strike a balance among the numerous links in the network. Finally, numerical results are provided to show that our proposed topology is a promising structure as it outperforms other topologies and it is superior to Fat-tree, BCube and DCell by approximately 24%, 16%, 8% respectively in terms of network throughput and latency. Moreover, P-Cube extremely outperforms Fat-tree, and BCube structures in terms of total cost, complexity of cabling and power consumption.

Cloud computing, which enables users to enjoy various Internet services provided by data centers (DCs) at anytime and anywhere, has attracted much attention. In cloud computing, however, service quality degrades with user distance from the DC, which is unfair. In this study, we propose a bandwidth allocation scheme based on collectable information to improve fairness and link utilization in DC networks. We have confirmed the effectiveness of this approach through simulation evaluations.

In data center networks, Quantized Congestion Notification (QCN) has been ratified as the standard congestion management mechanism in the link layer. Since QCN nonlinearly switches between the rate increase and decrease stages, it is very difficult to understand QCN in depth and provide theoretical guidelines on setting the buffer size of the QCN switch. This paper gives an explicit formula for the boundary of buffer occupancy of the QCN switch. Specifically, based on the fluid model of QCN, we first derive the uniformly asymptotic stability condition of the QCN system. Then, under the condition that QCN is uniformly asymptotically stable, we analyze the buffer occupancy of the QCN switch theoretically and show that the classic rule-of-thumb for buffer sizing is not suitable for QCN. Finally, simulations validate the accuracy of our theoretical results.

Query response times are critical for cluster computing applications in data centers. In this letter, we argue that to optimize the network performance, we should consider the latency of the flows suffered loss, which are called tardy flows. We propose two tardy flow scheduling algorithms and show that our work offers significant performance gains through performance analysis and simulations.

Address configuration is a key problem in data center networks. The core issue of automatic address configuration is assigning logical addresses to the physical network according to a blueprint, namely logical-to-device ID mapping, which can be formulated as a graph isomorphic problem and is hard. Recently years, some work has been proposed for this problem, such as DAC and ETAC. DAC adopts a sub-graph isomorphic algorithm. By leveraging the structure characteristic of data center network, DAC can finish the mapping process quickly when there is no malfunction. However, in the presence of any malfunctions, DAC need human effort to correct these malfunctions and thus is time-consuming. ETAC improves on DAC and can finish mapping even in the presence of malfunctions. However, ETAC also suffers from some robustness and efficiency problems. In this paper, we present GA-MAP, a data center networks address mapping algorithm based on genetic algorithm. By intelligently leveraging the structure characteristic of data center networks and the global search characteristic of genetic algorithm, GA-MAP can solve the address mapping problem quickly. Moreover, GA-MAP can even finish address mapping when physical network involved in malfunctions, making it more robust than ETAC. We evaluate GA-MAP via extensive simulation in several of aspects, including computation time, error-tolerance, convergence characteristic and the influence of population size. The simulation results demonstrate that GA-MAP is effective for data center addresses mapping.

Router virtualization is becoming more common as a method that uses network (NW) equipment effectively and robustly similar to server virtualization. Edge routers, which are gateways of core NWs, should be virtualized because they have many functions and resources just as servers do. To virtualize edge routers, a metro NW, which is a wide area layer-2 NW connecting each user's residential gateway to edge routers, must trace dynamic edge router re-allocation by changing the route of each Ethernet flow. Therefore, we propose a scalable centralized control architecture of a virtual layer-2 switch on a metro NW to trace virtual router re-allocation and use metro NW equipment effectively. The proposed scalable control architecture improves the centralized route control performance by processing in parallel on a flow-by-flow basis taking into account route information even in the worst case where edge routers fail. In addition, the architecture can equalize the load among parallel processes dynamically by using two proposed load re-allocation methods to increase the route control performance stably while minimizing the amount of resources for the control. We evaluate the scalability of the proposed architecture through theoretical analysis and experiments on a prototype and show that the proposed architecture increases the number of flows accommodated in a metro NW. Moreover, we evaluate the load re-allocation methods through simulation and show that they can evenly distribute the load among parallel processes. Finally, we show that the proposed architecture can be applied to not only large-scale metro NWs but also to data center NWs, which have recently become an important type of large-scale layer-2 NW.

In this paper, we propose a novel Autonomous Decentralized Control (ADC) scheme for indirectly controlling a system performance variable of large-scale and wide-area networks. In a large-scale and wide-area network, since it is impractical for any one node to gather full information of the entire network, network control must be realized by inter-node collaboration using information local to each node. Several critical network problems (e.g., resource allocation) are often formulated by a system performance variable that is an amount to quantify system state. We solve such problems by designing an autonomous node action that indirectly controls, via the Markov Chain Monte Carlo method, the probability distribution of a system performance variable by using only local information. Analyses based on statistical mechanics confirm the effectiveness of the proposed node action. Moreover, the proposal is used to implement traffic-aware virtual machine placement control with load balancing in a data center network. Simulations confirm that it can control the system performance variable and is robust against system fluctuations. A comparison against a centralized control scheme verifies the superiority of the proposal.

In recent years, technologies and markets related to data centers have been rapidly changing and growing. Data centers are playing an important role in ICT infrastructure deployment and promise to become common platforms for almost all social infrastructures. Even though research has focused on networking technologies, various technologies are needed to develop high-performance, cost-efficient, and flexible large-scale data centers. To understand those technologies better, this paper surveys recent research and development efforts and results in accordance with a data center network taxonomy that the authors defined.