Routing For Data Center Networks Research Articles

Secure paths based trustworthy fault‐tolerant routing in data center networks

Secure paths based trustworthy fault‐tolerant routing in data center networks

Fast, Scalable and Robust Centralized Routing for Data Center Networks

This paper presents a fast and robust centralized data center network (DCN) routing solution, called . For fast routing calculation, uses centralized controllers to collect/disseminate the network’s link-states (LS), and offload the actual routing calculation onto each switch. Observing that the routing changes can be classified into a few fixed patterns in DCNs which have regular topologies, we simplify each switch’s routing calculation into a table-lookup manner, i.e., comparing LS changes with pre-installed base topology and updating routing paths according to predefined rules. As such, the routing calculation time at each switch only needs 10s of us even in a large network topology containing 10K+ switches. For efficient controller fault-tolerance, purposely uses reporter switch to ensure the LS updates successfully delivered to all affected switches. As such, can use multiple stateless controllers and little redundant traffic to tolerate failures, which incurs little overhead under normal case, and keeps 10s of ms fast routing reaction time even under complex data-/control-plane failures. We design, implement and evaluate with extensive experiments on Linux-machine controllers and white-box switches. provides <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 1200x and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 100x shorter convergence time than current distributed protocol BGP and the state-of-the-art centralized routing solution, respectively. Furthermore, Primus maintains good routing controllability/manageability thanks to its centralized architecture, which enables us to build several advanced routing features in our testbed, including routing failure visualization and weighted-cost-multi-path routing.

Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks

This paper addresses the energy conservation problem in computing systems. The focus is on energy-efficient routing protocols. We formulated and solved a network-wide optimization problem for calculating energy-aware routing for the recommended network configuration. Considering the complexity of the mathematical models of data center networks and the limitations of calculating routing by solving large-scale optimization problems, and methods described in the literature, we propose an alternative solution. We designed and developed several efficient heuristics for equal-cost multipath (ECMP) and Valiant routing that reduce the energy consumption in the computer network interconnecting computing servers. Implementing these heuristics enables the selection of routing paths and relay nodes based on current and predicted internal network load. The utility and efficiency of our methods were verified by simulation. The test cases were carried out on several synthetic network topologies, giving encouraging results. Similar results of using our efficient heuristic algorithm and solving the optimization task confirmed the usability and effectiveness of our solution. Thus, we produced well-justified recommendations for energy-aware computing system design to conclude the paper.

A Decision Tree-Based Online Traffic Classification Method for QoS Routing in Data Center Networks

In data center networks, how to ensure the quality of service (QoS) of traffic effectively has become an important optimization goal. Traffic classification has been widely studied to guarantee network flow QoS since it provides a basis for the network performance level requirement of different flow types. However, traditional network traffic classification methods, which can only identify traffic type offline, have no ability for online classification and the in-time QoS guarantee. To solve this problem, we propose an online traffic classification for QoS routing (OCQR) framework based on programmable data plane in this paper. Firstly, in order to improve the accuracy of flow classification and reduce the impact on line-rate packet forwarding, the way of machine learning model offline training and online deployment is used to classify the network flow in real time. Based on the hardware foundation of programmable switch and the complexity of machine learning algorithm, a CART decision tree model is used to classify network flows. Secondly, in order to actually deploy and run the decision tree model in the programmable switch with limited computing and storage resources, we prune the decision tree model and optimize the leaf nodes to reduce the size of the decision tree and improve the classification recall rate. Finally, by building a P4 simulation environment, the recall rate of OCQR’s classified scheduling of some application flows reaches more than 98%, which is consistent with the evaluation results of the offline dataset. At the same time, compared with the classification method based on SDN, OCQR does not require additional bandwidth occupation, so it has less impact on network performance.

A secure data transmission scheme based on multi-protection routing in datacenter networks

The adoption of protection routing guarantees the existence of a loop-free alternate path for packet forwarding when a single link or node failure occurs. By Tapolcai's method, the presence of two completely independent spanning trees (CISTs for short) suffices to configure a protection routing. This article extends the idea of protection routing to involve more CISTs and attach a secure mechanism in the configuration, which we call the secure multi-protection routing scheme (SMPR-scheme). Then, we use the SMPR-scheme to deal with privacy-preserving data transmissions, such as downloading personal medical records, tax bills, or other private information. To evaluate the effectiveness of the SMPR-scheme, we develop a probabilistic model that allows some malicious nodes to collect information illegally through neighboring access. We experimented with SMPR-scheme on the BCube (i.e., the generalized hypercube) datacenter network. Simulation results show that data transmission using SMPR-scheme ensures confidentiality (i.e., no node other than the recipient can receive the complete message) and effectively resists privacy collection even under malicious infringement.

Topology Independent Multipath Routing for Data Center Networks

Data center communication networks are characterized by fast and intensive traffic changes, to the extent that a statistical approach to routing has significant drawbacks. This paper proposes the first routing solution that takes into account every data center flow. In order to achieve the required performance, the proposed algorithm operates in the data plane, i.e. in the packet processing pipeline of the programmable packet forwarding chips. Route updates are triggered by the arrival of new flows, and are immediately transferred to the upstream switches in order for them to direct newly arriving flows along the best path. This enables the proposed algorithm to handle fast and intensive traffic changes, such as large-scale flow incast communication patterns which historically presented challenges to the data center routing algorithms. With the explicit knowledge of the current path state, the proposed algorithm can improve routing performance in the data center topologies used today. Additionally, being able to operate with high performance in arbitrary topology, the proposed solution can enable the introduction of new topologies into the data center networks, instead of topologies designed with the goal of supporting equal-cost multipath routing which are dominant in modern data centers.

An adaptive failure recovery mechanism based on asymmetric routing for data center networks

As the infrastructure of high-performance computing, the data center network plays an important role. As network failures occur frequently, data center networks demand highly performed, robust, and energy-efficient failure recovery mechanisms. Despite process, the existing work still has a huge scope to improve to satisfy these requirements. The backup-based failure recovery schemes reserve backup paths in advance, which results in a large energy consumption under normal network conditions. In order to solve the energy consumption problem, the existing adaptive failure recovery schemes are proposed to calculate the rerouting path of the traffic on the failed link, which reduces the energy consumption. However, most adaptive fault recovery solutions apply multi-path routing to calculate the re-routing path. As multi-path routing cannot detect the congestion status of the path under the asymmetric topology caused by link failures, the network is congested, which ends up in less robustness of the network. In view of this, we design and evaluate AFRM, a novel adaptive failure recovery mechanism that overcomes these challenges. AFRM uses asymmetrical routing to calculate the re-routing path by being congestion-aware and is more robust to topological asymmetries compared with existing schemes. The asymmetrical routing dynamically schedules flows to the path with the least marginal cost, which makes AFRM much more energy-efficient. Additionally, AFRM achieves fast link failure detection based on hash storage and flow table matching. Evaluations show that AFRM can do the trade-off between failure recovery time and energy consumption, reduce flow completion time, and increase network throughput compared with existing schemes.