Traffic Scheduling Research Articles

Neuro-fuzzy-based mathematical model of dispatching of an industrial railway junction

In any transport system, especially at industrial railway junctions, it is fundamentally important to build an effective timetable (traffic schedule) to regulate traffic flows. The task is complicated by the high dimensionality of the railway network of the node, the large number of variable parameters associated with scheduling the use of a traction resource (locomotives) during operation for sorting wagons and transporting payloads (ore, fuel, finished products and empty wagons). The problem is that most plotting problems are NP-hard, i.e. the algorithms for solving them, used to automate the process, may require an unacceptably long execution time by traditional methods of solving this problem (sequential, using reference information; method of thread laying). The article deals with the issues of building a mathematical model for dispatching an industrial railway junction to minimize the time of using locomotives in order to increase the efficiency of its operation. The mathematical model uses the technique of neuro-fuzzy computing to determine the parameters for identifying fuzzy systems and calculating the priorities of operations in the framework of creating a flexible schedule for the decision support system of the dispatching service. The results of modeling and recommendations on the use of the developed methodology are presented.

A QoS-aware scheduling with node grouping for IEEE 802.11ah

The recent IEEE 802.11ah amendment has proven to be suitable for supporting large-scale devices in Internet of Things (IoT). It is essential to provide a minimum level of Quality of Service (QoS) for critical applications such as industrial automation and healthcare. In this paper, we propose a QoS-aware Medium Access Control (MAC) layer solution to enhance network reliability and reduce critical traffic latency by an adaptive station grouping and a priority traffic scheduling scheme. First, a link layer representation of traffic categories as per the delay and reliability requirements is proposed. Second, a novel backoff size-based slot scheduling scheme for Restricted Access Window (RAW) is proposed to support QoS. Third, a grouping scheme is proposed to calculate the current traffic load and balance it among different RAW groups. Finally, a Markov-chain model is developed to study the throughput and latency behaviors of the traffic generated from the critical application. The proposed protocol shows significant delay improvement for priority traffic. The overall throughput performance improves up to 12.7% over the existing RAW grouping scheme.

HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic

Abstract. We propose a new deep-learning architecture HIDRA2 for sea level and storm tide modeling, which is extremely fast to train and apply and outperforms both our previous network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. The architecture of HIDRA2 employs novel atmospheric, tidal and sea surface height (SSH) feature encoders as well as a novel feature fusion and SSH regression block. HIDRA2 was trained on surface wind and pressure fields from a single member of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric ensemble and on Koper tide gauge observations. An extensive ablation study was performed to estimate the individual importance of input encoders and data streams. Compared to HIDRA1, the overall mean absolute forecast error is reduced by 13 %, while in storm events it is lower by an even larger margin of 25 %. Consistent superior performance over HIDRA1 as well as over general circulation models is observed in both tails of the sea level distribution: low tail forecasting is relevant for marine traffic scheduling to ports of the northern Adriatic, while high tail accuracy helps coastal flood response. Power spectrum analysis indicates that HIDRA2 most accurately represents the energy density peak centered on the ground state sea surface eigenmode (seiche) and comes a close second to SCHISM in the energy band of the first excited eigenmode. To assign model errors to specific frequency bands covering diurnal and semi-diurnal tides and the two lowest basin seiches, spectral decomposition of sea levels during several historic storms is performed. HIDRA2 accurately predicts amplitudes and temporal phases of the Adriatic basin seiches, which is an important forecasting benefit due to the high sensitivity of the Adriatic storm tide level to the temporal lag between peak tide and peak seiche.

DEEP LEARNING-DRIVEN DIFFERENTIATED TRAFFIC SCHEDULING IN CLOUD-IOT DATA CENTER NETWORKS

The development of 5G technology has enabled the cloud-internet of things (IoT) to impact all areas of our lives. Sensors in cloud-IoT generate large-scale data, and the demand for massive data processing is also increasing. The performance of a single machine can no longer meet the needs of existing users. In contrast, a data center (DC) integrates computing power and storage resources through a specific network topology and satisfies the need to process massive data. Regarding large-scale heterogeneous traffic in DCs, differentiated traffic scheduling on demand reduces transmission latency and improves throughput. Therefore, this paper presents a traffic scheduling method based on deep Q-networks (DQN). This method collects network parameters, delivers them to the environment module, and completes the environment construction of network information and reinforcement learning elements through the environment module. Thus, the final transmission path of the elephant flow is converted based on the action given by DQN. The experimental results show that the method proposed in this paper effectively reduces the transmission latency and improves the link utilization and throughput to a certain extent.

A Scientometric Review of Mobility-on-Demand Car-Sharing Systems

This article gives a scientometric analysis of research advances in mobility-on-demand (MoD)systems. By reviewing 191 articles with diverse perspectives from the past 10 years, a scientometric study is conducted using CiteSpace, giving an integrated knowledge mind mapping of MoD systems. The results indicate that the major contributions in MoD systems field are mainly from the United States, Singapore, China, and Germany. Meanwhile, pivotal institutions, journals, and article keywords are discussed. MoD system knowledge domains are concentrated in the categories of demand and service, system optimization, impacts and factors, and response and choice. The development of MoD systems is evolving from taxis and minibuses to autonomous vehicles. In particular, research interest in autonomous MoD systems is rapidly growing. This analysis gives a comprehensive and integral knowledge mind mapping, which contributes to the MoD system information network and provides valuable insights into future research directions and developments for investigators and software developers in the field of vehicle-sharing and traffic scheduling.

Data Center Traffic Scheduling Strategy for Minimization Congestion and Quality of Service Guaranteeing

According to Cisco’s Internet Report 2020 white paper, there will be 29.3 billion connected devices worldwide by 2023, up from 18.4 billion in 2018. 5G connections will generate nearly three times more traffic than 4G connections. While bringing a boom to the network, it also presents unprecedented challenges in terms of flow forwarding decisions. The path assignment mechanism used in traditional traffic scheduling methods tends to cause local network congestion caused by the concentration of elephant flows, resulting in unbalanced network load and degraded quality of service. Using the centralized control of software-defined networks, this study proposes a data center traffic scheduling strategy for minimization congestion and quality of service guaranteeing (MCQG). The ideal transmission path is selected for data flows while considering the network congestion rate and quality of service. Different traffic scheduling strategies are used according to the characteristics of different service types in data centers. Reroute scheduling for elephant flows that tend to cause local congestion. The path evaluation function is formed by the maximum link utilization on the path, the number of elephant flows and the time delay, and the fast merit-seeking capability of the sparrow search algorithm is used to find the path with the lowest actual link overhead as the rerouting path for the elephant flows. It is used to reduce the possibility of local network congestion occurrence. Equal cost multi-path (ECMP) protocols with faster response time are used to schedule mouse flows with shorter duration. Used to guarantee the quality of service of the network. To achieve isolated transmission of various types of data streams. The experimental results show that the proposed strategy has higher throughput, better network load balancing, and better robustness compared to ECMP under different traffic models. In addition, because it can fully utilize the resources in the network, MCQG also outperforms another traffic scheduling strategy that does rerouting for elephant flows (namely Hedera). Compared with ECMP and Hedera, MCQG improves average throughput by 11.73% and 4.29%, and normalized total throughput by 6.74% and 2.64%, respectively; MCQG improves link utilization by 23.25% and 15.07%; in addition, the average round-trip delay and packet loss rate fluctuate significantly less than the two compared strategies.

Approaches to Creating a Driver Decision Support System for Digital Analysis of Railway Infrastructure Based on Machine Learning and Machine Vision Algorithms

The paper considers the issues of creating a driver decision support system for digital analysis of the railway infrastructure based on machine learning and machine vision algorithms, which will take into account and analyse the given traffic schedule, infrastructure capabilities, dispatch centre teams, statuses of the nearest traffic participants for unmanned safe control of electric rolling stock. A detailed review of existing control systems in railway transport is made, which are based on technical vision.

Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network

Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification. Different metrics have been adopted to evaluate the proposed classifier's effectiveness: accuracy, precision, recall, Matthews Correlation Coefficient (MCC), and F1-Score. Compared with a convolutional neural network (CNN), the simulation results confirmed that the DSNN model could enhance traffic classification accuracy by 5%. The efficiency of the priority model also has been demonstrated in terms of Round Trip Time (RTT).

О развитии информационных систем в ОАО «РЖД» (на примере Восточно- Сибирской железной дороги)

The article discusses the alterations in the measures for throughput and carrying capacity, identified shortcomings in the information process. A brief overview of the software of AS EBL (Automated System Electronic Bill of Lading), AS TOM (Automated System of Transportation Operational Management), DM IL (Dynamic Model for Infrastructure Loading), AMS LP (Automated Management System of Local Performance), ETS URAL VNIIZHT (Executed Traffic Schedule URAL VNIIZHT) and other IT technologies of JSC “Russian Railways” used on the East Siberian Railway is provided. The conclusion is made about the advantages and disadvantages of the DM IL user interface.

A Hybrid Deep Learning Method Based on CEEMDAN and Attention Mechanism for Network Traffic Prediction

Accurate prediction of network traffic trends is important for self-management, intelligent scheduling and network resource optimization of base stations. Network traffic prediction is a prerequisite for intelligent scheduling of base stations, and accurate prediction will be beneficial for improving network utilization and energy saving in scheduling. In this paper, a hybrid deep learning method for network traffic prediction, CEEMDAN-TGA which consists of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Temporal Convolutional Network (TCN), Gated Recurrent Unit (GRU), and Attention Mechanism is proposed. Firstly, CEEMDAN is introduced to decompose the original network traffic data into different modes, then reconstruct the modes into trend sequence and noise sequence. Secondly, TCN is used to extract the short-term local features in the network traffic, GRU is used to obtain the long-term data-dependent features, and the attention mechanism is used to improve the prediction accuracy and stability. Finally, through the comparison of experiments, the prediction effect and accuracy of the proposed method are verified to have significant advantages, and the network traffic scheduling strategy is proposed on the basis of prediction.

Моделирование процесса передачи трафика реального времени с использованием планировщика и функцией контроля доставки в программно-конфигурируемых сетях

The article is described a model of the real-time traffic transmission process using a scheduler and a delivery control function in software defined networks (SDN) based on Petri nets and the CPN Tools package. The Petri subnets of the traffic scheduler, the delivery time validator have been developed and described, the subnets of the flow tables have been modified, the subnets of the comparison and search rules in the flow table, traffic classification, queues and queue manager have been modified. Experimental modeling was carried out with different switch loads, and the results of the developed method were compared with the classical method of transmitting heterogeneous traffic to a SDN. According to the data obtained, the method of the process of transmitting real-time traffic using a scheduler and a delivery control function to a SDN has proven its effectiveness. The effectiveness of the proposed method lies in the fact that the load of the switch remains constant due to a more flexible configuration of the scheduler schedule, rather than rigid time intervals of timeouts, and the possibility of early transmission of elastic traffic is also realized until the time of delivery of real-time traffic has come.

ENERGY EFFICIENCY OF PUBLIC TRANSPORT FOR SUSTAINABLE URBAN MOBILITY

The article discusses the main problems associated with energy efficiency in creating a sustainable mobility system in cities. Urban public transport occupies a special place in meeting the needs of the population to move around the city. Solutions are considered that can help save energy resources, improve the ecological state and speed up the delivery of city residents. The possible effects of the use of alternative types of public transport and vehicles with an alternative source of energy, the integration of the mode of operation of various types of transport, the coordination of traffic schedules are analyzed.

Application of hybrid genetic algorithm in large traffic scheduling in SDN architecture

Application of hybrid genetic algorithm in large traffic scheduling in SDN architecture

Application of hybrid genetic algorithm in large traffic scheduling in SDN architecture

Application of hybrid genetic algorithm in large traffic scheduling in SDN architecture

Reinforcement Learning-Based Delay-Aware Path Exploration of Parallelized Service Function Chains

The parallel processing of the service function chain (SFC) is expected to provide better low-delay service delivery, because it breaks through the bottleneck of traditional serial processing mode in which service delay increases linearly with the SFC length. However, the provision of parallelized SFC (PSFC) is much more difficult due to the unique construction of PSFCs, inevitable parallelization overhead, and delay balancing requirement of PSFC branches; therefore, existing mechanisms for serial SFC cannot be directly applied to PSFC. After a comprehensive review of recent related work, we find that traffic scheduling mechanisms for PSFCs is still lacking. In this paper, a delay-aware traffic scheduling mechanism (DASM) for PSFCs is proposed. DASM first transforms PSFC into several serial SFCs by releasing the upstream VNF constraints so as to handle them independently while keeping their parallel relations. Secondly, DASM realizes delay-aware PSFC traffic scheduling based on the reinforcement learning (RL) method. To the best knowledge of the authors, this is the first attempt to address the PSFC traffic scheduling problem by transforming them into independent serial SFCs. Simulation results show that the proposed DASM outperforms the advanced PSFCs scheduling strategies in terms of delay balance and throughput.

Application of bayesian networks to estimate the probability of a transfer at a public transport stop

Optimizing transfers during public transport operations is one of the essential components of improving the quality of transport. Several factors influence the passenger's perception of a transfer: from the personal characteristics of the user of transport services to the parameters of the route network, trip characteristics and the design of transfer stops. The method of constructing Bayesian networks was used as one of the effective methods for solving problems of forecasting complex systems to find the relationship between different types of input data that affect the probability of making a transfer at a stop. The need for a transfer arises for a passenger when two reasons are combined: the need to make a trip between two transport areas and the lack of a direct public transport route between these transport areas. The number of needs for trip will depend on the number of residents in the departure zone, and the probability of not having a direct route will depend on the total number of routes departing from this zone. A simulation was carried out in the PTV Visum software environment (on the example of Lviv city) to determine the impact of these factors on the probability of changing at a stop. As a result, data were obtained on the total amount of passenger exchange at the stops of the public transportation system with distribution into the number of passengers disembarking at the stop, the number of passengers transferring at this stop, and the number of passengers going (up to 200 m) to another stop to transfer. The average waiting time for a transfer at a stop depends on both the number of routes passing through the stop and the regularity of traffic. Strict adherence to traffic schedules helps to reduce the average waiting time for a transfer. A comparison of the results of calculating the probability of a transfer at one of the stops using calculations based on field observation data and using modeling was carried out to check the adequacy of the modeling. The calculated probability is 0.16, the simulated probability is 0.12.

ILP-based multiperiod flow routing and scheduling method in time-sensitive network

Time-sensitive network (TSN) provides end-to-end data transfer with very low latency and high reliability on the basis of Ethernet. It is suitable for time-sensitive applications and will be widely used in scenarios such as autonomous driving and industrial Internet. When the existing time-sensitive flow routing and scheduling methods do not consider the shared bandwidth of the flow triggered by different cycle times, the possibility of conflict is different, which leads to the inaccuracy of the index to measure the scheduling bottleneck. This paper analyses the different cycle time-sensitive flow between the possibility of conflict, puts forward a kind of applicable to passes based on integer linear programming cycle time-sensitive traffic scheduling algorithm, according to the characteristics of the cycle time-sensitive flow carries on the preliminary classification and sorting, and developed a series of scheduling constraints, implementation is sensitive to time flow of packet scheduling. The experiment can be conducted from the three perspectives of average end-to-end delay, maximum load, and flow scheduling. The proposed routing method was compared with the existing benchmark routing algorithm evaluation. The results show that the proposed method can significantly improve the capacity of the network sensitive to time flow, reduce load scheduling by optimizing the network bottleneck at the same time, and improve the schedulability.

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.

Analysis and modeling of developing passenger traffic on public transport routes of urban transport systems

Problem. To solve the problems of rational organization of work of urban passenger transport on the routes and determine the modes of vehicles work and labor of drivers of motor transport enterprises when carrying out transportation of the city population. It is necessary to know all the input conditions for creating traffic schedules on the route. One of the main parameters is the existing traffic volumes on a certain public transport route. In order to improve the management of transportation on the routes of urban passenger transport, various approaches to their survey were considered, the problem of studying traffic volumes for further drawing up traffic schedules was identified. In this article, the task was to analyze the work of a separate route and passenger traffic on it. Goal. The goal is studying passenger traffic on a separate public transport route, studying the regularities of traffic volumes and their dependence on operating time. Methodology. When fulfilling the task, the procedure by which the input data is processed is given. Having examined the known methods and principles of applying certain groups of methods to solve various types of problems, to solve the problem of organizing work on the route, namely, to distribute changes in the operation of vehicles for the transportation of passengers, we opted for a readable tabular method of examining passenger traffic. Thus, an incoming sequence of values was obtained, which we presented as time series levels. An analysis of incoming time series was carried out, their modeling using an adaptive algorithmic method. Results. A method of using computer mathematics systems for modeling and predicting input time series was developed, during which the solution process was programmed and the results of adaptive algorithmic modeling of passenger traffic dependencies on time were obtained, which can be used to build schedules for the movement of vehicles during passenger transportation by public transport. Originality. Passenger traffic on a separate route was investigated, the methodology of this study was presented, all components of the time series were taken into account and modeled. The graphic representation was provided. This article has developed programs for performing modeling and forecasting using adaptive algorithmic models. Practical value. This provides an opportunity to investigate the passenger traffic of any passenger transport route, carrying out transportation, sequences of volume whose values have similar components of the time series. The result of these studies is building a model and obtaining forecast values, which plays a large role in management of road passenger transport.

A Hybrid Traffic Scheduling Strategy for Time-Sensitive Networking

The traffic scheduling mechanism in Time-Sensitive Networking (TSN) is the key to guaranteeing the deterministic transmission of traffic. However, when time-sensitive traffic and non-time-sensitive traffic are transmitted together, traffic scheduling conflicts are easy to occur in TSN. As a result, the deterministic transmission of time-sensitive traffic will be disrupted, and non-time-sensitive traffic may be preempted for a long time. To optimize the performance of multi-type hybrid traffic scheduling in TSN, we firstly establish a collaborative scheduling framework that incorporates Time Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF) mechanisms. We then design a traffic shaping method in this framework based on Least Laxity First (LLF), which considers traffic characteristics to dynamically arrange the time slot injection sequence for different types of traffic. Finally, the traffic schedulability is evaluated based on the scheduling constraints of different types of traffic. Compared with the existing scheduling strategies, the proposed hybrid traffic scheduling strategy can schedule more non-time-sensitive traffic and achieve better delay performance of rate-constrained traffic in different hybrid traffic scenarios. When the number of flows is 100, the time slot injection ratio is increased by 24.3% compared with the LLF_TAS method.