Equal-cost Multi-path Routing Research Articles

SDNRoute: Proactive routing optimization in Software Defined Networks

Despite opening attractive perspectives, the concept of Software Defined Networking raises doubts about the performance and practical feasibility. To contradict these concerns, we propose a deployment-ready system aimed at proactive and periodic optimization of flow paths. The modular system consists of modules responsible for traffic prediction, static optimization, measurements, flow management, and validation of optimization results. To make the system efficient, we resolved several scientific issues and proposed novel and valuable solutions, for example methods for efficient proactive flow management and periodic re-optimization of routing policies. Simultaneously, to make the system production-ready and create a reliable research environment, we provide solutions to several technical obstacles.We validate the proposed system with three network topologies, each with three load levels, following real-life traffic models. We consider Equal-Cost Multi-Path Routing as a baseline. The results indicate that the system allows network operators to handle more traffic (packet loss reduced by up to 30%), improve quality of service (less congested links resulted in even 2.5 times lower latency), and reduce operational expenses (energy consumption lowered by up to 10%).

Software Defined Multicast Using Segment Routing in LEO Satellite Networks

The emerging low earth orbit (LEO) broadband satellite networks are creating new opportunities to enable superior video distribution. With numerous satellites deployed, broadband constellations are capable of distributing videos across the globe by efficient multicasting techniques. However, existing work only studied IP multicast for broadband constellations, which suffer from limited scalability and tree performance. With recent breakthroughs in software defined networking, novel software defined multicasting (SDM) techniques manage to achieve efficient data transfer through intelligent and granular management, outperforming traditional IP Multicast. This paper leverages software defined multicasting in the promising broadband constellations to empower satellite-based Internet video distribution. Based on rectilinear Steiner trees, this paper proposes a novel software defined multicasting framework for broadband satellite networks. In addition, this paper designs simple, agile, and scalable multicast segment routing protocols implementing source routing and equal cost multipath routing. The proposed protocols also adapt to frequent member updates and network failures with efficient tree recovery and local rerouting mechanisms. Comprehensive experiments demonstrate the effectiveness and efficiency of our approach when compared with traditional algorithms.

Enhancing Network Availability: An Optimization Approach

High availability is vital for network operators to ensure reliable services. Network faults can disrupt functionality and require quick recovery. Multipath networking enhances availability through load balancing and optimal link utilization. However, equal-cost multipath (ECMP) routing has limitations in effectively using multipaths, decreasing network availability. This paper proposes a three-phase disjoint-path framework that improves availability by directing traffic flows through separate paths. The framework provides effective load balancing and meets various service requirements. It includes the Optimization phase for identifying optimal multipath solutions, the Path Separation phase for dividing the multipath into working and backup sets, and the Quality Assessment phase for evaluating the robustness of both sets using topological metrics and micro-based characteristics. The simulations demonstrate the proposed framework’s validation and effectiveness in enhancing network availability.

GraphSAGE-Based Multi-Path Reliable Routing Algorithm for Wireless Mesh Networks

Wireless mesh networks (WMN) promise to be an effective way to solve the “last mile” access problem on the Internet of Things (IoT) and the key to next-generation wireless networks. The current routing algorithms of WMN are difficult to adapt to complex environments and guarantee the reliable transmission of services. Therefore, this paper proposes a reliable routing algorithm that combines the improved breadth-first search and a graph neural network, namely GraphSAGE. The algorithm consists of two parts: (1) A multi-path routing algorithm based on the improved breadth-first search. This algorithm can continuously iterate link information based on network topology and output all shortest paths. (2) A GraphSAGE-based performance optimization algorithm. This algorithm creates a method to generate network labels for supervised training of GraphSAGE. Then, the network labels and GraphSAGE are used to learn graph features to obtain the value of network performance for each shortest path. Finally, the path with the best network performance is selected for data transmission. Simulation results show that in the face of complex environments, the proposed algorithm can effectively alleviate network congestion, improve throughput, and reduce end-to-end delay and packet loss rate compared with the traditional shortest-path routing algorithm and the Equal-Cost Multi-Path routing (ECMP).

A port-based forwarding load-balancing scheduling approach for cloud datacenter networks

Today’s datacenter networks (DCNs) scale is rapidly increasing because of the wide deployment of cloud services and the rapid rise of edge computing. The bandwidth consumption and cost of a DCN are growing sharply with the extensions of network size. Thus, how to keep the traffic balanced is a key and challenging issue. However, the traditional load balancing algorithms such as Equal-Cost Multi-Path routing (ECMP) are not suitable for high dynamic traffic in cloud DCNs. In this paper, we propose a port-based forwarding load balancing scheduling (PFLBS) approach for Fat-tree based DCNs with some new features which can overcome the disadvantages of the existing load balancing methods in the following aspects. Firstly, we define a port-based source-routing addressing scheme, which decreases the switch complexity and makes the table-lookup operation unnecessary. Secondly, based on this addressing scheme, we proposed an effective routing mechanism which can obtain multiple available paths for flow scheduling based in Fat-tree. All the path information is saved in servers and each server only needs to maintain its own path information. Thirdly, we propose an efficient algorithm to implement large flows scheduling dynamically in terms of current link utilization ratio. This method is suitable for cloud DCNs and edge computing, which can reduce the complexity of the switches and the power consumption of the whole network. The experiment results indicate that the PFLBS approach has better performance compared with the ECMP, Hedera and MPTCP approaches, which decreases the flow completion time and improves the average throughput significantly. PFLBS is simple and can be implemented with a few signaling overheads.

Multi-Path Routing in Green Multi-Stage Upgrade for Bundled-Links SDN/OSPF-ECMP Networks

This paper considers the novel problem of upgrading a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">legacy</i> network into a Software Defined Network (SDN) over multiple stages and saving energy in the upgraded network, or hybrid SDN. That is, in each stage, the problem at hand is to select and replace <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">legacy</i> switches with SDN switches and reroute traffic to power off as many unused cables as possible to save energy. Also, the operator must consider: (i) the available budget at each stage, (ii) maximum path delays, (iii) maximum link utilization, (iv) per-stage increase (decrease) in traffic size (upgrade cost), and (v) the Open Shortest Path First - Equal Cost Multi-Path protocol. This paper addresses two multi-path routing scenarios: 1) non-link-disjoint and 2) link-disjoint. It outlines a Mixed Integer Program and a heuristic algorithm for each scenario. The experimental results show that: (i) both solutions produce only up to 0.63% higher energy saving in scenario-1 than in scenario-2, (ii) the mixed integer program (heuristic algorithm) for both scenarios give an energy saving up to 71.93% (71.64%), (iii) using a larger budget and/or number of stages can increase the energy saving, and (iv) the saving achieved by the heuristic solution for each scenario is within 4% from the optimal saving.

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.

Avoiding bottlenecks in networks by short paths

Data center networks are typically characterized by high density communication components that process and exchange large amount of information using shortest paths. Mostly, data centers network topologies contain multi-rooted tree with multiple equal cost shortest paths between pairs of hosts. Usually, data center networks operation is based on the result of shortest path algorithms and per-flow static hashing which may cause poor network utilization rates with some links becoming congested while some parts of the network are underused. This work presents a flow scheduling algorithm that exploits the path diversity in data center topologies and dynamically reroutes large flows through less congested shortest and non-shortest paths based on the current state of the network without causing packets reordering. The algorithm aims to minimize flows latency while maximizing the network utilization rates. Results show that the algorithm proposed in this work reduces flows completion time by 13–24% over Equal-Cost Multi-Path routing, while improving the average network utilization by up to 10%.

Multi-path Allocation Scheduling Optimization Algorithm for Network Data Traffic Based on SDN Architecture

AbstractThe explosive growth of network data traffic puts new demands on traffic scheduling. In this paper, the scheduling algorithm based on the software-defined network (SDN) architecture is studied. Firstly, the SDN architecture was introduced, then an SDN-based adaptive multi-path load balancing algorithm was proposed and finally the algorithm was simulated on the Mininet simulation platform to compare the performance of the proposed algorithm and traditional equal-cost multi-path routing algorithm by using Ryu as the controller. It was found that the proposed algorithm had greater throughput, higher bandwidth utilization and shorter transmission time in allocation scheduling of network data traffic, which could effectively reduce network congestion and ensure the reliability of the network. This study verifies the effectiveness of the proposed method in allocation scheduling of network data traffic and provides some theoretical bases for the further application of the SDN architecture-based allocation scheduling algorithm.

Deep Q-Learning for Routing Schemes in SDN-Based Data Center Networks

In order to adapt to the rapid development of cloud computing, big data, and other technologies, the combination of data center networks and SDN is proposed to make network management more convenient and flexible. With this advantage, routing strategies have been extensively studied by researchers. However, the strategies in the controller mainly rely on manual design, the optimal solutions are difficult to be obtained in the dynamic network environment. So the strategies based on artificial intelligence (AI) are being considered. This paper proposes a novel routing strategy based on deep Q-learning (DQL) to generate optimal routing paths autonomously for SDN-based data center networks. To satisfy the different demands of mice-flows and elephant-flows in data center networks, deep Q networks are trained for them respectively to achieve low latency and low packet loss rate for mice-flows as well as high throughput and low packet loss rate for elephant-flows. Furthermore, with the consideration of the distribution of traffic and the limited resources of data center networks and SDN, we choose port rate and flow table utilization to describe the network state. Simulation results show that compared with Equal-Cost Multipath (ECMP) routing and Selective Randomized Load Balancing (SRL)+FlowFit, the proposed routing scheme can reduce both the average delay of mice-flows and average packet loss rate, while increase the average throughput of elephant-flows.

MaxPass: Credit-based multipath transmission for load balancing in data centers

Various applications require the data center networks to carry their traffic efficiently. The data center networks usually have a hierarchical topology and exhibit distinct traffic patterns, which is different from the traditional Internet. These features have driven the data center networks to reduce the flow completion time (FCT) and to achieve high throughput. One of the possible solutions is balancing network loads across multiple paths by leveraging transport mechanisms like equal-cost multipath (ECMP) routing. ECMP allows flows to exploit multiple paths by hashing the metadata of the flows. However, due to the random nature of hash functions, ECMP often distributes the traffic unevenly, which makes it hard to utilize the links' full capacity. Thus, we propose an adaptive load balancing mechanism for multiple paths in data centers, dubbed MaxPass, to complement ECMP. A sender adaptively selects and dynamically changes multiple paths depending on the current network status like congestion. To monitor the network status, the corresponding receiver transmits a probe packet periodically to the sender; its loss indicates a traffic congestion. We carry out the quantitative analysis on the ns-2 simulator to show that MaxPass can improve the FCT and the throughput.

Performance evaluation of elephant flow predictors in data center networking

The dynamic characteristics and unpredictability of the traffic generated by applications and services have given rise to some problems in data center networks (DCN). In particular, the concentration of elephant flows in DCNs can lead to a load imbalance because the equal-cost multi-path (ECMP) routing maps them to the same path. This can result in packet discard, which generates packet re-transmission, causes additional latency, and further degrades link performance. In this paper, we evaluate the performance of different elephant flow predictors with the use of real DCN traces. A traffic time series from a Facebook data center is used to model the elephant flow predictors on a short-term basis and to provide the descriptive statistics and inferences the flows. The paper proposes a hybrid prediction model by combining aspects of the FARIMA and the Recurrent Neural Network (FARIMA-RNN) models. A methodology based on the rank of the prediction accuracy metrics is applied to compare the performance of the hybrid model with the ARIMA, GARCH, RBF, MLP, and LSTM models. The comparison allows the selection of an appropriate model for the implementation of elephant flow scheduling techniques in DCNs. Results show that the FARIMA-RNN model presents lower error rates than the other predictors.

Improving Multipath Routing of TCP Flows by Network Exploration

Ethernet switched networks are widely used in enterprise and data center networks. However, they have some drawbacks, mainly that to prevent loops, they cannot take advantage of multipath topologies to balance traffic. Several multipath routing proposals use link-state protocols and equal cost multi-path routing (ECMP) to distribute the load over multiple paths. But, these proposals are complex and prone to flow collisions that may degrade performance. This paper studies TCP-path, a protocol that employs a different approach. It uses a distributed network exploration mechanism based on broadcasting the TCP-SYN packet to identify and select the fastest available path to the destination host, on the fly. Our evaluation shows that it improves on ECMP by up to 70% in terms of throughput for elephant flows and by up to 60% in terms of flow completion time for mouse flows. Indeed, network exploration offers a better, yet simple alternative to ECMP-based solutions for multipath topologies. In addition, we also study TCP-path for elephant flows (TFE) which restricts TCP-path application to elephant flows to reduce the exploration broadcast overhead and the size of forwarding tables thus improving its scalability. Although elephant flows represent a small fraction (about 5%) of total flows, they have a major impact on overall performance, as we show in our evaluation. TFE reduces both the overhead incurred during path setup and the size of the forwarding tables by a factor of almost 20. Moreover, it achieves results close to those obtained by TCP-path for elephant flows, especially when working with high loads, and yields significant improvements for all types of flow at medium- and high-load levels.

Methods for Predicting Behavior of Elephant Flows in Data Center Networks

Several Traffic Engineering (TE) techniques based on SDN (Software-defined networking) proposed to resolve flow competitions for network resources. However, there is no comprehensive study on the probability distribution of their throughput. Moreover, there is no study on predicting the future of elephant flows. To address these issues, we propose a new stochastic performance evaluation model to estimate the loss rate of two state-of-art flow scheduling algorithms including Equalcost multi-path routing (ECMP), Hedera besides a flow congestion control algorithm which is Data Center TCP (DCTCP). Although these algorithms have theoretical and practical benefits, their effectiveness has not been statistically investigated and analyzed in conserving the elephant flows. Therefore, we conducted extensive experiments on the fat-tree data center network to examine the efficiency of the algorithms under different network circumstances based on Monte Carlo risk analysis. The results show that Hedera is still risky to be used to handle the elephant flows due to its unstable throughput achieved under stochastic network congestion. On the other hand, DCTCP found suffering under high load scenarios. These outcomes might apply to all data center applications, in particular, the applications that demand high stability and productivity.

Improved Load Balancing on Software Defined Network-based Equal Cost Multipath Routing in Data Center Network

Equal Cost Multipath Routing (ECMP) is a routing application where all available paths between two nodes is utilized by statically mapping each path to possible traffics between source and destination hosts in a network. This configuration can lead to congestion if there are two or more traffics being transmitted into paths with overlapping links, despite the availability of less busy paths. Software Defined Networking (SDN) has the ability to increase the dynamicity of ECMP by allowing controller to monitor available bandwidths of all links in the network in real-time. The measured bandwidth is then implemented as the basis of the calculation to determine which path a traffic will take. In this research, a SDN-based ECMP application that can prevent network congestion is made by measuring available bandwidth of each available paths beforehand, thus making different traffics transmitted on non-overlapped paths as much as possible. The proposed scheme increased the throughput by 14.21% and decreased the delay by 99% in comparison to standard ECMP when congestion occurs and has 75.2% lower load standard deviation in comparison to round robin load balancer.

Modeling Transport Layer Protocol Behaviour to Improve Data Center Network Performance

Today, there is a generalized standard usage of internet for all.The devices via multiple technologies that facilitates to provide few communication methods to scholars to work with. By forming multiple paths in the data center network, latest generation data centers offer maximum bandwidth with robustness. To utilize this bandwidth, it is necessary that different data flows take separate paths. In brief, a single-path transport seems inappropriate for such networks. By using Multipath TCP, we must reconsider data center networks, with a diverse approach as to the association between topology, transport protocols, routing. Multipath TCP allows certain topologies that single path TCP cannot use. In newer generation data centers, Multipath TCP is already deployable using extensively deployed technologies such as Equal-cost multipath routing. But, major benefits will come when data centers are specifically designed for multipath transports. Due to manifold of technologies like Cloud computing, social networking, and information networks there is a need to deploy the number of large data centers. While Transport Control Protocol is the leading Layer-3 transport protocol in data center networks, the operating conditions like high bandwidth, small-buffered switches, and traffic patterns causes poor performance of TCP. Data Center TCP algorithm has newly been anticipated as a TCP option for data centers which address these limitations. It is worth noting that traditional TCP protocol.

Load Balancing in Data Center Networks: A Survey

Data center networks usually employ the scale-out model to provide high bisection bandwidth for applications. A large amount of data is required to be transferred frequently between servers across multiple paths. However, traditional load balancing algorithms like equal-cost multi-path routing are not suitable for rapidly varying traffic in data center networks. Based on the special data center topologies and traffic characteristics, researchers have recently proposed some novel traffic scheduling mechanisms to balance traffic. In this paper, we present a comprehensive survey of recent solutions for load balancing in data center networks. First, recently proposed data center network topologies and the studies of traffic characteristics are introduced. Second, the definition of the load-balancing problem is described. Third, we analyze the differences between data center load balancing mechanisms and traditional Internet traffic scheduling. Then, we present an in-depth overview of recent data center load balancing mechanisms. Finally, we analyze the performance of these solutions and discuss future research directions.

Flow Optimization in Data Centers With Clos Networks in Support of Cloud Applications

The proliferation of cloud computing has forced data centers to support a large number of dynamic resources such as virtual machines and storage volumes from any physical location within the data center. This has led to highly volatile and unpredictable traffic patterns. The emergence of cloud applications that exchange large volumes of information have created large persistent flows that need to coexist with other types of traffic. As a result, a number of data center topologies have been proposed to support cloud applications. One of the topologies that has gained prominence is the Clos network. On one hand, Clos networks provide many nice properties, such as equidistant hops, bandwidth, and latency. On the other hand the routing of diverse traffic flows still remains an issue. Current routing methods such as equal-cost multi-path (ECMP) routing that rely on flow hashing over equal cost paths do not take into consideration traffic volume nor network state. In this paper, we investigate routing in Clos topologies. We formulate optimal flow routing in Clos networks as a binary multicommodity flow problem and present optimized flow re-routing, a modified version of the binary multicommodity flow problem that uses a heuristics approach to minimize hash collisions. We also propose FlowFit, a practical optimization method that uses network state to optimally re-assign flows to links. Our experimental and simulation results show a significant improvement in bisection bandwidth and flow completion time over ECMP.

A lightweight container-based virtual time system for software-defined network emulation

Container-based network emulation offers high fidelity and a scalable testing environment to bridge the gap between research ideas and real-world network applications. However, containers take their notions of time from the physical system clock, and thus the time-stamped events from different containers are multiplexed to reflect the scheduling serialization by the Linux operating system. Conjoining the emulator and other simulators is also challenging due to the difficulties of synchronizing the virtual simulation clock with the physical system clock. Virtual time systems for network emulation shed light on both issues. In this paper, we develop a lightweight container-based virtual time system in Linux Kernel. We use time dilation to trade time with system resources by precisely scaling the time of interactions between containers and physical devices. We develop a time freezer to enable the precise pause and resume of an emulation experiment, which offers the virtual time support to interface with simulators for close synchronization. We integrate the virtual time system into a software-defined networking emulator, Mininet, and evaluate the system accuracy, scalability, and overhead. Finally, we use the virtual-time-enabled emulation testbed to conduct a case study of equal-cost multi-path routing protocol analysis in a data center network.

SDN-Enabled Game-Aware Routing for Cloud Gaming Datacenter Network

Cloud gaming or gaming as a service, the newest entry in the online gaming world, leverages the well-known concept of cloud computing to provide real-time gaming services to players. This gaming paradigm provides affordable, flexible, and high performance solutions for end users with constrained computing resources and enables them to play high-end graphic games on low-end thin clients, because it renders everything in the cloud and simply streams the resulting high-quality video to the player. Despite its advantages, cloud gaming’s quality of experience suffers from high and unstable end-to-end delay. According to this fact of cloud gaming, datacenters are in charge of performing complex rendering and video encoding computations, delivering high-quality gaming experience to gamers which requires an efficient and smart resource allocation mechanism to allot resources (e.g., memory and network bandwidth) to gaming sessions consistent with their requirements. In this paper, we propose a bi-objective optimization method to find an optimum path for packet transmission within a data center by minimizing delay and maximizing bandwidth utilization. We use a metaheuristic model, called analytic hierarchy process, to solve the NP-complete optimization problem. The resulting method is an analytic hierarchy process-based game aware routing (AGAR) scheme that considers requested game type and requirements in terms of delay and bandwidth to select the best routing path for a game session in a cloud gaming network. The method executes within a software defined network controller, which affords it a global view of the data center with respect to communication delay and available bandwidth. Simulation results indicate that the AGAR can reduce the end-to-end delay by up to 9.5% compared with three other conventional representative methods: the delay-based Dijkstra, the equal cost multi-path routing, and the Hedera routing algorithms. In addition, we demonstrate that the proposed method assigns game flows to network paths and OpenFlow switches in a balanced manner that prevents potential network bottlenecks.