Effective TCP Flow Management Based on Hierarchical Feedback Learning in Complex Data Center Network

Many studies focusing on improving Transmission Control Protocol (TCP) flow control realize a more effective use of bandwidth in data center networks. They are excellent ways to more effectively use the bandwidth between clients and back-end servers. However, these schemes cannot achieve the total optimization of bandwidth use for data center networks as they do not take into account the path design of TCP flows against a hierarchical complex structure of data center networks. To address this issue, this paper proposes a TCP flow management scheme specified a hierarchical complex data center network for effective bandwidth use. The proposed scheme dynamically controls the paths of TCP flows by reinforcement learning based on a hierarchical feedback model, which obtains an optimal TCP flow establishment policy even if both the network topology and link states are more complicated. In evaluation, the proposed scheme achieved more effective bandwidth use and reduced the probability of TCP incast up to 30% than the conventional TCP flow management schemes: Variant Load Balancing (VLB), Equal Cost Multi Path (ECMP), and Intelligent Forwarding Strategy Based on Reinforcement Learning (IFS-RL) in the complex data center network.

Fault-Tolerant Secure Routing of B H n -Based Data Center Networks

As the core infrastructure of cloud computing, a large scale of the data center networks (DCNs), which consist of millions of servers with high capacity, suffer from node failure such that the reliability is deteriorated. Malicious group could inevitably compromise the quality and reliability of data; thus, how to ensure the security routing of data is an urgent practical problem. As models for large-scale DCNs, it is worth mentioning the balanced hypercube, which is well-known for its strong connectivity, regularity, and a smaller diameter. Each of which makes a balanced hypercube a trustworthy model to deal with data traffic and provides a certain degree of fault-tolerance as well. In this paper, we use the balanced hypercube as a model for the data center networks and design a reliable safety level by referring to different safety levels of related subgraph. This subgraph contains the source and destination nodes, and the shortest feasible paths are located so that the reliable transmission is achieved. Then, we get that the length of fault-tolerant safety routing of data center networks based on balanced hypercube is always no greater than the Hamming distance plus two. Experiment shows that our fault-tolerant security routing scheme is more effective in the same reliable network environment of DCNs.

Oddlab: fault-tolerant aware load-balancing framework for data center networks

Data center networks (DCNs) act as critical infrastructures for emerging technologies. In general, a DCN involves a multi-rooted tree with various shortest paths of equal length from end to end. The DCN fabric must be maintained and monitored to guarantee high availability and better QoS. Traditional traffic engineering (TE) methods frequently reroute large flows based on the shortest and least-congested paths to maintain high service availability. This procedure results in a weak link utilization with frequent packet reordering. Moreover, DCN link failures are typical problems. State-of-the-art approaches address such challenges by modifying the network components (switches or hosts) to discover and avoid broken connections. This study proposes Oddlab (Odds labels), a novel deployable TE method to guarantee the QoS of multi-rooted data center (DC) traffic in symmetric and asymmetric modes. Oddlab creatively builds a heuristic model for efficient flow scheduling and faulty link detection by exclusively using the gathered statistics from the DCN data plane, such as residual bandwidth and the number of installed elephant flows. Besides, the proposed method is implemented in an SDN-based DCN without altering the network components. Our findings indicate that Oddlab can minimize the flow completion time, maximize bisection bandwidth, improve network utilization, and recognize faulty links with sufficient accuracy to improve DC productivity.

Design of Four-Core Uncoupled Multicore Fiber for Next-Generation Inter-Data Center Networks

The increasing demands within and between the data centers used for data traffic has required. Efficient links are important to data center applications for supporting the unlimited demand. Transmission capacity of single-mode fiber (SMF) is limited by fiber nonlinearity which prevents the increasing transmission power and finite amplifier bandwidth. Single-mode multi-core fibers (SM-MCFs) that are expected to overcome the current limitation of optical communication capacity. However, the inter-core crosstalk still has an effect on SM-MCF, which can limit the transmission of the inter-data center. In this paper, the design of four-core uncoupled multicore fiber is discussed for next generation inter-data center networks in order to support the unlimited use of data traffic in the future. The objective of this paper is to determine the appropriate range of core radius and core pitch, which are taken into consideration to reduce the inter-core crosstalk inside the optical fiber. These parameters can be able to improve various constraints to achieve the best multi-core fibers design. From the simulation concerned with the inter-core crosstalk, the experiment results show that the range of core pitch is at 47.5 μm to 50 μm and the range of core radius starts from 4.5 μm to 5.5 μm, that can achieve with crosstalk lower than – 30 dB/100 km for the future inter-data center networks.