Link Congestion Research Articles

Understanding congestion propagation by combining percolation theory with the macroscopic fundamental diagram

The science of cities aims to model urban phenomena as aggregate properties that are functions of a system’s variables. Following this line of research, this study seeks to combine two well-known approaches in network and transportation science: (i) The macroscopic fundamental diagram (MFD), which examines the characteristics of urban traffic flow at the network level, including the relationship between flow, density, and speed. (ii) Percolation theory, which investigates the topological and dynamical aspects of complex networks, including traffic networks. Combining these two approaches, we find that the maximum number of congested clusters and the maximum MFD flow occur at the same moment, precluding network percolation (i.e. traffic collapse). These insights describe the transition of the average network flow from the uncongested phase to the congested phase in parallel with the percolation transition from sporadic congested links to a large, congested cluster of links. These results can help to better understand network resilience and the mechanisms behind the propagation of traffic congestion and the resulting traffic collapse.

Implementation of Link Failover on Metronet Network PT. Telkom Indonesia (Persero) Based on Ipv4 and OSPF

Metro ethernet is a data communication network owned by PT. Telkom Indonesia (Persero) through distribution channels, one of which is Open Shortest Path First (OSPF). As technology develops, IPv4 is used as the core addressing protocol for every communicating network device and has the responsibility of finding the route taken by packets to reach their destination. The problem that occurs in the data communication network is the absence of stable connectivity when there are links between cities experiencing downtime, resulting in several link congestion on the data packet path. To overcome the above problems, this study analyzed IPv4-based metro ethernet networks through the OSPF routing protocol using Graphical Network Simulator-3 (GNS-3). This study implements a failover system as a backup link in the event of a Jakarta-Bogor downlink. Based on the simulation and test results, the failover link shows a success status and functions to minimize network connection breakdowns. This is because the use of OSPF routing isOSPF routing canen the main link is down, so the path will be diverted via the backup link. QoS measurements using a single link obtained an average throughput of 195 Kbps, while dual links obtained an average throughput of 258 Kbps. This is because the distance from the source to the destination router is passed by more single links than dual links. Overall, the measurement shows that having a backup link can increase the stability of the data communication network connection when it is down and tested according to ETSI standards

Measurement outlier-resistant mobile robot localization using multiple Doppler-azimuth radars under round-robin protocol

This paper is concerned with the measurement outlier-resistant mobile robot localization problem by using multiple Doppler-azimuth radars under round-robin protocol (R-RP). In the considered robot localization system, multiple Doppler-azimuth radars are equipped on the robot platform to produce the measurement including the Doppler frequency shift and the azimuth. In order to assuage communication link congestion, the R-RP is used. For mitigating the influence of outliers, a time-varying state estimator is constructed which contains a saturation function with variable saturation levels. This paper aims at seeking out a practicable yet effective solution to the addressed robot localization problem by devising the constructed estimator which can assure that, over a finite horizon, the localization error satisfies the given H∞ performance index. By constructing an appropriate Lyapunov function, the sufficient condition, which can guarantee the localization error to fulfill the given H∞ performance, is established. Then, by resorting to the solution to a set of linear matrix inequalities, the constructed estimator can be devised. In the light of the estimator design strategy proposed in this paper, the corresponding robot localization algorithm is developed. At last, some simulations are conducted to testify the usefulness of the developed robot localization algorithm.

Efficient Origin-Destination Estimation Using Microscopic Traffic Simulation with Restricted Rerouting

Traffic simulators are utilized to solve a variety of traffic-related problems. For such simulators, origin-destination (OD) traffic volumes as mobility demands are required to input, and we need to estimate them. The authors regard an OD estimation as a bi-level programming problem, and apply a microscopic traffic simulation model to it. However, the simulation trials can be computationally expensive if full dynamic rerouting is allowed, when employing multi-agent-based models in the estimation process. This paper proposes an efficient OD estimation method using a multi-agent-based simulator with restricted dynamic rerouting to reduce the computational load. Even though, in the case of large traffic demand, the restriction on dynamic rerouting can result in heavier congestion. The authors resolve this problem by introducing constraints of the bi-level programming problem depending on link congestion. Test results show that the accuracy of the link traffic volume reproduced with the proposed method is virtually identical to that of existing methods but that the proposed method is more computationally efficient in a wide-range or high-demand context.

IQoR-LSE: An Intelligent QoS On-Demand Routing Algorithm With Link State Estimation

With the rapid development of Internet applications, diversified Quality of Service (QoS) has been required in packet routing to meet the demand of various types of applications. In this article, an Intelligent QoS on-demand Routing (IQoR) framework has been proposed to support multiclass QoS Provisioning for packet forwarding. In addition, we present an IQoR with Link State Estimation (IQoR-LSE) algorithm with the assistance of link congestion inference to guide the exploration of action space in deep reinforcement learning, to seek optimal routing policies. The IQoR-LSE algorithm is proposed to solve the non-convergence problem at exploring the high-dimensional action space by jointly estimating the link congestion. Extensive simulations show that IQoR-LSE algorithm outperforms other benchmark routing algorithms with efficient learning and a significant reduction in average delay, jitter and packet loss.

Deep Reinforcement Learning-Based QoS Optimization for Software-Defined Factory Heterogeneous Networks

The routing algorithm based on a single routing metric parameter is difficult to meet the Quality of Services (QoS) requirements of multi-source data flows in the software-defined factory heterogeneous network, resulting in link congestion and waste of network resources. To solve the problem, this paper proposes a QoS optimization method for software-defined factory heterogeneous networks based on the double deep Q network (DDQN). First, a QoS optimization architecture is proposed for software-defined factory heterogeneous networks, and the optimization function for network latency and load balancing is established. Then, the DDQN algorithm is used to obtain the optimal paths of multi-source data flows, and the optimal path is uniformly issued by the software-defined network controller. Experiments showed that the proposed algorithm has good convergence and generalizability. Compared with the existing algorithms, it outperforms in network latency, network jitter, and network throughput, improving network load balancing and routing efficiency. This research is able to provide a solution to the QoS optimization and dynamic traffic scheduling in smart heterogeneous networks and provide a technical reference point for realizing customized smart factories.

Effects of second-best tradable credit scheme on transportation network for travel mobility management

The second-best tradable credit scheme is studied to analyse its effects on travel mobility by a bilevel programming model. The transport authority optimises the credit charging scheme to minimise the total system travel time and travellers choose paths to minimise their generalised travel costs. A genetic algorithm with some improvements on the selection operator is used to solve the proposed bilevel programming model. In numerical experiments, we discuss the implications of travellers’ path choice behaviour and traffic flow distribution under the second-best tradable credit scheme with different combinations of credit-charging links. The results show that the credit charging scheme with all links is obviously better than other schemes in the total system travel time. Besides, the credit charging scheme using congested links or duplicate links of the maximum traffic flow path as credit-charging links is effective in reducing traffic flow on congested links and mitigating the congestion of the whole transportation network.

Modeling inter-DC bulk data transfer in optical networks with edge storage

In communication networks that span large geographical regions, transfer of bulk data is often costly and hard to manage. One prominent reason for that is bandwidth contention among long-lasting flows, and between long-lasting and short-lived ones on inter-datacenter links. Delaying the transfer of bulk data to off-peak hours by temporarily storing transfer at edge of the network can help reduce bandwidth contention during peak hours, and improve overall bandwidth utilization and network utility. Although extensive research has been conducted on its use, to date no model has been developed to quantify the performance of storage assisted bulk transfers across multiple congested links. This research gap has limited our understanding of the operation of storage assisted transfer under different network conditions. In this paper, we model bulk data transfer in inter-datacenter optical networks with edge storage, and derive closed-form equations for bulk data transfer on inter-DC links that exhibit diurnal traffic patterns and varying peak hours. Our study reveals that when the data transfer requests arrive uniformly in a day, the probability of a successful transfer increases linearly with the time data is allowed to wait in edge storage. Our study also shows that this probability decreases linearly with β + D, where β and D are the durations of background traffic peak and size of data transfers, respectively, when the network is moderately loaded. And, the benefit of storage will barely increase when the allowed waiting time goes beyond β + D.

Reducing blocking probability and QoT violation in dynamic elastic optical networks via load-aware margin selection

In this paper, we study the effect of load-aware margin selection on the performance of elastic optical networks (EONs) in dynamic service provisioning. First, a mixed-integer linear programming (MILP) formulation is proposed as a benchmark for dynamic routing, modulation level, spectrum, and power assignment (RMSPA). In this formulation, nonlinear interferences (NLIs) in optical fibers and the noise of amplifiers are considered. In addition, a margin which is calculated based on the network load is considered to estimate the additional noise which may degrade the signal quality of transmission (QoT) during its life time. The objective of this formulation is to minimize the spectrum usage and impairments of/from the signal which is optimized by choosing the optimum channel among a number of pre-calculated channels based on the QoT constraints. In order to reduce the RMSPA run time, heuristic algorithms are also proposed. The algorithms employ K-least weighted paths for routing. The defined weights are distance, the amount of traffic load, and a mixture scheme. Furthermore, the algorithms try to minimize the spectrum usage by choosing the highest possible modulation level while considering a specific criterion. The assumed criteria are: i) choosing the lowest possible power spectral density (PSD) in a discrete set, ii) selecting the optimum power among the set, iii) obtaining maximum SNR. We simulated the proposed algorithms and the MILP, and compared their performances on NSFNET and US Backbone topologies. The results reveal that the constrained best SNR scheme has the best performance among the heuristics and its performance is close to MILP, especially in higher loads. Moreover, results show that considering the links congestion leads to blocking reduction. We also simulated the first-fit based algorithms without considering NLIs which assign the modulation based on: i) considering only amplifier noise and self-channel interference (SCI), ii) experimental distance thresholds, and iii) overestimating the NLI. The results imply that the proposed MILP and heuristic algorithms outperform the other algorithms in terms of the summation of outage (probability of QoT violation) and blocking probability.

ReLFA: Resist link flooding attacks via renyi entropy and deep reinforcement learning in SDN-IoT

Link flooding attack (LFA) is a fresh distributed denial of service attack (DDoS). Attackers can cut off the critical links, making the services in the target area unavailable. LFA manipulates legal low-speed flow to flood critical links, so traditional technologies are difficult to resist such attack. Meanwhile, LFA is also one of the most important threats to Internet of things (IoT) devices. The introduction of software defined network (SDN) effectively solves the security problem of the IoT. Aiming at the LFA in the software defined Internet of things (SDN-IoT), this paper proposes a new LFA mitigation scheme ReLFA. Renyi entropy is to locate the congested link in the data plane in our scheme, and determines the target links according to the alarm threshold. When LFA is detected on the target links, the control plane uses the method based on deep reinforcement learning (DRL) to carry out traffic engineering. Simulation results show that ReLFA can effectively alleviate the impact of LFA in SDN IoT. In addition, the rerouting time of ReLFA is superior to other latest schemes.

Beacon Synchronization-Based Multi-Channel With Dynamic Time Slot Assignment Method of WSNs for Mechanical Vibration Monitoring

Wireless Sensor Networks (WSNs) for mechanical vibration monitoring has been proposed for harsh environment monitoring purposes. However, links congestion makes the realization of reliable and energy-efficient transmission a challenge for such WSNs. To ease bandwidth pressure, a beacon synchronization-based multi-channel with dynamic time slot assignment method is proposed in this paper. Firstly, the network is separated into two opposing groups based on working state to maximize transmission performance. Secondly, the channel is allocated based on interference minimization from adjacent channels. To ensure the two opposing groups work individually, the nodes are synchronously triggered by transmission beacon, then nodes backoff and delay for a period of time to transmit data. Moreover, a dynamic timeslot assignment method is proposed to decrease channel collision. Finally, the experimental results demonstrate that the proposed methods can effectively schedule the network for data transmission via multi-channel, and the channel utilization performs better than CSMA/CA and TDMA.

Fast and scalable routing protocols for data center networks

Data center networks may comprise tens or hundreds of thousands of nodes, and, naturally, suffer from frequent software and hardware failures as well as link congestions. Packets are routed along the shortest paths with sufficient resources to facilitate efficient network utilization and minimize delays. In such dynamic networks, links frequently fail or get congested, making the recalculation of the shortest paths a computationally intensive problem. Various routing protocols were proposed to overcome this problem by focusing on network utilization rather than speed. Surprisingly, the design of fast shortest-path algorithms for data centers was largely neglected, though they are universal components of routing protocols. Moreover, parallelization techniques were mostly deployed for random network topologies, and not for regular topologies that are often found in data centers. The aim of this paper is to improve scalability and reduce the time required for the shortest-path calculation in data center networks by parallelization on general-purpose hardware. We propose a novel algorithm that parallelizes edge relaxations as a faster and more scalable solution for popular data center topologies.

A quasi-contagion process modeling and characteristic analysis for real-world urban traffic network congestion patterns

The congestion propagation pattern in urban traffic networks has long been viewed as a complex question. Inspired by the fact that virus spread patterns are similar to traffic congestion propagation in some ways, this paper proposes a congestion propagation model (SIS-CP) to describe the congestion propagation patterns in large-scale traffic networks with very few parameters. In this SIS-CP model, the attribute of the real traffic network in the actual operation process is considered, a method based on Hilbert–Huang transform (HHT-based) is proposed to determine traffic states of links, and the congestion propagation patterns are analyzed. The real-world data of ‘DiDi Chuxing GAIA Open Dataset Initiative of Chengdu city from March to June is used to calibrate and validate the proposed models. The results show that the basic and limit value of congestion links’ ratio do exist in real traffic networks; the relationship between congestion links’ ratio and its derivative is conformed to the parabolic function; the HHT-based method can determine traffic states of links accurately; and the SIS-CP model can describe the congestion propagation patterns correctly.

MFUC: Mitigating Transient Congestion for Multi-flow Updating in SDN

Through the logically centralized control plane, software-defined network (SDN) provides giant convenience for the design of the network strategies, like load balancing, fault recovery, etc. However, when deploying those strategies on the data plane, transient link congestions may occur since the updating process among the distributed switches is asynchronous. To address this issue, we propose a congestion-aware mechanism for multi-flow updating in SDN. We first obtain the real-time traffic size of each flow to be updated through the link monitoring model. Then we use the congestion-aware algorithm to find the potential congested link and construct the dynamic dependency graph of the updating process, to generate the final updating sequence. Finally, the meter table is configured to limit the flow of crucial predecessor nodes that may have congestion deadlocks to mitigate the transient congestion.

Distributed uplink cache for improved energy and spectral efficiency in B5G small cell network.

The advent of content-centric networks and Small Cell Networks (SCN) has resulted in the exponential growth of data for both uplink and downlink transmission. Data caching is considered one of the popular solutions to cater to the resultant challenges of network congestion and bottleneck of backhaul links in B5G networks. Caching for uplink transmission in distributed B5G scenarios has several challenges such as duplicate matching of contents, mobile station’s unawareness about the cached contents, and the storage of large content size. This paper proposes a cache framework for uplink transmission in distributed B5G SCNs. Our proposed framework generates comprehensive lists of cache contents from all the Small Base Stations (SBSs) in the network to remove similar contents and assist uplink transmission. In addition, our framework also proposes content matching at a Mobile Station (MS) in contrast to an SBS, which effectively improves the energy and spectrum efficiency. Furthermore, large size contents are segmented and their fractions are stored in the distributed cache to improve the cache hit ratio. Our analysis shows that the proposed framework outperforms the existing schemes by improving the energy and spectrum efficiency of both access and core networks. Compared to the existing state of the art, our proposed framework improves the energy and spectrum efficiency of the access network by 41.28% and 15.58%, respectively. Furthermore, the cache hit ratio and throughput are improved by 9% and 40.00%, respectively.

ALBRL: Automatic Load-Balancing Architecture Based on Reinforcement Learning in Software-Defined Networking

Due to the rapid development of network communication technology and the significant increase in network terminal equipment, the application of new network architecture software-defined networking (SDN) combined with reinforcement learning in network traffic scheduling has become an important focus of research. Because of network traffic transmission variability and complexity, the traditional reinforcement-learning algorithms in SDN face problems such as slow convergence rates and unbalanced loads. The problems seriously affect network performance, resulting in network link congestion and the low efficiency of inter-stream bandwidth allocation. This paper proposes an automatic load-balancing architecture based on reinforcement learning (ALBRL) in SDN. In this architecture, we design a load-balancing optimization model in high-load traffic scenarios and adapt the improved Deep Deterministic Policy Gradient (DDPG) algorithm to find a near-optimal path between network hosts. The proposed ALBRL uses the sampling method of updating the experience pool with the SumTree structure to improve the random extraction strategy of the empirical-playback mechanism in DDPG. It extracts a more meaningful experience for network updating with greater probability, which can effectively improve the convergence rate. The experiment results show that the proposed ALBRL has a faster training speed than existing reinforcement-learning algorithms and significantly improves network throughput.

An Ant Colony Optimization-Based Routing Algorithm for Load Balancing in LEO Satellite Networks

Satellite networks can provide a wider service range and lower delay than traditional terrestrial optical fiber networks. However, due to the bursty characteristic of the Internet traffic and the distributive feature of satellite links, traffic-intensive areas often suffer from link congestion while links in other areas are underutilized, i.e., the traffic imbalance problem in LEO satellite networks. In this paper, an ant colony optimization routing algorithm with window reduction for LEO satellite networks, ACORA-WR, is proposed to achieve load balancing. ACORA-WR limits the movement of the ant colony to a specific range and comprehensively considers the path distance, transmission direction, and link load to find a path with low delay and overhead. Simulation results verify that the proposed ACORA-WR scheme demonstrates high data delivery ratio and network throughput, while ensuring low average delay and network transmission overhead.

Spatiotemporal Approach for Evaluating the Vehicle Restriction Policy with Multi-Sensor Data

Vehicle restriction has been one of the most popular policies in alleviating traffic congestion and air pollution in mega cities. This paper evaluates the effect of the vehicle restriction policy on urban networks in Shanghai based on multi-source traffic data. First, before–after comparisons are conducted to investigate the impact of the restriction policy on travelers’ adaptation behaviors. A macroscopic fundamental diagram (MFD)-based approach is then applied to analyze the network performance under different levels of restrictions that have been implemented over the years. A spatial regression method is used to account for the spatial and temporal reasons of network performance under different restriction policy stages. Following the first increase of the vehicle restriction time in Shanghai (2015), the network-wide traffic performance significantly improved in the short term (within one month), but the effect was not substantial in the long term (one year). Affected drivers tended to shift either their travel routes or departure time. No significant increase of travelers on the public transport system was found after the extension of the vehicle restriction time. Both urban expressways and surface road networks became less efficient after a further extension of restricted time in 2016. Population distribution, interactions among different modes, distribution of parking facilities, and locations of ramps can account for the increase of congested links at the neighborhood level, which in turn aggravated the imbalance of the entire network after the extension of vehicle restriction time.

P4Resilience: Scalable Resilience for Multi-failure Recovery in SDN with Programmable Data Plane

Network failures are common in all kinds of networks, from data centers to wide-area networks. The current network mainly adopts reactive and proactive failure recovery schemes. The reactive scheme needs to contact to the controller to implement failure recovery after detecting a link failure, which will cause a large recovery delay. Whereas, the proactive scheme pre-calculates and installs backup paths in the switch to accelerate failure recovery, which consumes a lot of switch storage. Programmable data plane creates a possibility to solve network failure problem. To realize an efficient failure recovery while keeping switch storage overhead low, we present P4Resilience, a hybrid multi-failure recovery system combining SDN and programmable data plane. Firstly, P4Resilience encapsulates the backup path information into the packet header to perform failure recovery, which can greatly reduce the switch storage overhead. Secondly, P4Resilience implements loop-free backup path algorithm to calculate backup paths for each link to realize strong resilience to multi-link failure. Finally, we develop an optimization traffic balancing algorithm to reallocate bandwidth resources for flows to avoid potential congestion after failure recovery. Experimental results show that compared with the traditional failure recovery mechanisms, P4Resilience significantly reduces switch storage overhead for backup baths. Meanwhile, P4Resilience effectively relieves link congestion caused by failure recovery and guarantees the protection performance with a higher failure recovery rate and less packet loss rate.

Load balancing routing algorithm based on extended link states in LEO constellation network

As an important part of satellite communication network, LEO satellite constellation network is one of the hot research directions. Since the nonuniform distribution of terrestrial services may cause inter-satellite link congestion, improving network load balancing performance has become one of the key issues that need to be solved for routing algorithms in LEO network. Therefore, by expanding the range of available paths and combining the congestion avoidance mechanism, a load balancing routing algorithm based on extended link states in LEO constellation network is proposed. Simulation results show that the algorithm achieves a balanced distribution of traffic load, reduces link congestion and packet loss rate, and improves throughput of LEO satellite network.