Traffic Scheduling Research Articles

DiffTREAT: Differentiated Traffic Scheduling Based on RNN in Data Centers

Transmission schemes in data centers are supposed to accurately distinguish flow types for different scheduling. However, prior efforts failed to meet the needs at all levels in a cost-effectively way. Nor the existing schemes proved applicable to all the diverse scenarios or dynamic traffic patterns. Therefore, we proposed <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Diff</u> erentiated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tr</u> affic sch <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> duling in d <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ta cen <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ers (DiffTREAT) based the Recurrent Neural Network (RNN), aiming to simplify the transmission in the dynamic and diverse network scenarios. First, DiffTREAT utilizes deep learning methods for traffic classification and flow size prediction. Second, according to the classified results of flows, DiffTREAT adopts multilevel priority queues to ensure the preferential transmission of latency-sensitive flows while optimizing the overall average flow completion time (FCT). Third, DiffTREAT employs the network cache to increase the capacity of data center networks (DCN), which effectively fights against the traffic burst and improves the throughput of latency-insensitive flows. DiffTREAT has been tested in different topologies in the contexts of diverse network loads and real-world workloads. Experiment results showed that compared with state-of-the-art schemes, DiffTREAT yielded both the lower average flow completion time for latency-sensitive flows and the higher throughput for latency-insensitive flow.

Research on path selection and flow scheduling of time-sensitive networks for power substations

In view of the problem that the flow of intelligent substation traffic scheduling increases, resulting in the transmission delay also increases, this paper proposed a joint optimization method of Time Sensitive Network (TSN) path selection and scheduling, which combines the resource sensing K-Shortest Path Algorithm (KPA) path selection algorithm and the constraint based gating method. This method can select an optimal transmission path for TT flows and plan the transmission path for non TT flows. Based on the scheduling mechanism of TSN, constraints and optimization objective functions are established, and a gating scheduling method based on constraints is proposed. Finally, the test and verification platform of the scheme is designed and built. Through the test and verification platform, the data stream transmission delay and delay jitter of the scheme are analyzed, and the algorithms are compared and analyzed from the aspects of running time and schedulability. With the continuous evolution of the follow-up TSN technology and the in-depth promotion of the power industry, more and more TSN key technologies will be applied to the smart grid.

Improved Quality of Online Education Using Prioritized Multi-Agent Reinforcement Learning for Video Traffic Scheduling

The recent global pandemic has transformed the way education is delivered, increasing the importance of video-based online learning. However, this puts a significant pressure on the underlying communication networks and the limited available bandwidth needs to be intelligently allocated to support a much higher transmission load, including video-based services. In this context, this paper proposes a Machine Learning (ML)-based solution that dynamically prioritizes content viewers with heterogeneous video services to increase their Quality of Service (QoS) and perceived Quality of Experience (QoE). The proposed approach makes use of the novel Prioritized Multi-Agent Reinforcement Learning solution (PriMARL) to decide the prioritization order of the video-based services based on networking conditions. However, the performance in terms of QoS and QoE provisioning to learners with different profiles and networking conditions depends on the type of scheduler employed in the frequency domain to conduct the scheduling and the radio resource allocation. To decide the best approach to be followed, we employ the proposed PriMARL solution with different types of scheduling rules and compare them with other state-of-the-art solutions in terms of throughput, delay, packet loss, Peak Signal-to-Noise Ratio (PSNR), and Mean Opinion Score (MOS) for different traffic loads and characteristics. We show that the proposed solution achieves the best user QoE results.

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational efficiency. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established, demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios. Numerical experiments are conducted on extensive scenarios with different arrival flows, where the Constraint Programming technique is employed to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

Cell Zooming for Hybrid VLC-RF Cellular Networks With High Energy Efficiency

The integration of visible light communication (VLC) and radio frequency (RF) communication achieves complementary advantages of high data rates and wide coverage, while the power consumption should also be considered. In this letter, a genetic algorithm for cell zooming is proposed to realize high energy efficiency in hybrid VLC-RF cellular networks. Based on locations and data rates of multiple users, the proposed algorithm will optimize transceiver association and load balancing for appropriate traffic scheduling. Simulation results reveal that the proposed algorithm can reduce total power consumption for both single and hybrid access, providing solutions for practical deployment of hybrid systems.

Application study of ant colony algorithm for network data transmission path scheduling optimization

Abstract With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within ±2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.

Assessing the traffic scheduling method for time-sensitive networking (TSN) by practical implementation

Time-sensitive networking (TSN), as standardized and maintained by the IEEE 802.1 Task Group, enhances the real-time and deterministic capabilities of the Ethernet. However, traffic scheduling is not standardized, and is being extensively researched. Most studies are theoretical in nature; practical validation studies are rare. To fill this gap, we first constructed a real-world TSN-based process automation system and presented fine-grained guidelines regarding how a traffic scheduling method (TSM) can be deployed in industrial facilities. We experimentally investigated the feasibility of our method, and compared its performance to that of a commercial TSN scheduler. The results show that our method precisely schedules traffic to satisfy firm real-time requirements and achieves ultra-low latency by eliminating the queuing delays. The scheduling calculation times for 500 random cases show that our TSM can appropriately schedule flows in milliseconds, more than 571-fold faster than the commercial scheduler.

Improving multimodal passenger transportation technology under the wartime conditions in Ukraine

Abstract. Problem. Ukraine has a rather complex transport network, so the mismatch between the work of carriers when transferring from one mode of transport to another can create a large number of inconveniences for passengers. Issuance of various travel documents, the complexity of processing luggage and its movement between vehicles, a more thorough check of documents and luggage by employees of law enforcement agencies at border crossings and transfer points lead to an increase in travel time. On the other hand, ensuring the demand for some main directions of transportation can only be through the use of several modes of transport, so there is a need to improve the existing logistics technology for multimodal passenger transportation.&#x0D; Goal. The goal of this publication is to improve the technology of multimodal passenger transportation under martial law Methodology. Analytical research methods were used to form the relationship between the train delay at the border station and the number of passengers in it. Mathematical modulation methods were used in the formation of a new agreed schedule for the arrival of international passenger trains at the Kyiv-Passenger transport hub. Results. The research has shown that even under the current wartime conditions, it is possible to improve the operation of various types of passenger transport and the consistency of their traffic schedules to better meet the needs and quality of passenger transportation. The authors of the article analyzed the state and causes of delays in passenger trains at the Mostyska-2 border crossing, through which the largest railway passenger flows between Ukraine and the EU countries currently follow. Based on the analysis, the dependence of the train delay time at the border station on the number of passengers in the train was revealed, and in order to reduce the duration of these delays, it was proposed to improve the existing technology for calculating the travel time of a passenger train by introducing and using value F, corresponding to the train delay time at the border station per passenger. Originality. It is suggested to improve the model for organizing the interaction of different types of transport in the transport interchange Kyiv-passenger hub, by introducing a new value φ based on the dependence of the train delay time at the border station on the number of passengers transported in it, taking into account the specifics of the introduction of martial law in Ukraine. Practical value. The introduction and integration of the proposed technology into the existing mathematical models for the formation of a rationally coordinated schedule of multimodal passenger transportation will significantly reduce the waiting time for passengers at transfer points, rationalize the capacity and population of the rolling stock and increase the financial profit for carriers, it can also be used for the development of the system, information technologies and the use of a "single ticket".

Smart Utilities IoT-Based Data Collection Scheduling

The Internet of Things is an ecosystem that connects billions of smart devices, meters, and sensors. These devices and sensors collect and share data for use and evaluation by organizations in different industry sectors. Humans may use the IoT to live and work more intelligently and gain total control over their lives. Consequently, IoT can be used to connect devices and integrate them with new digital technologies for customers. On the other hand, smart utility companies in the electric, gas, and water sectors need to deliver services more efficiently and analyze their operations in a way that can help optimize performance, detect growing problems in real time, and initiate fixes to avoid unplanned service interruptions. Building actual smart metering networks is costly and time-consuming. Therefore, in this paper, a new Smart Utilities Traffic Scheduling Algorithm (SUTSA) is proposed. To minimize the system complexity, the model is based on narrowband power line communication, in which a wired hidden network sends data across power lines. A simulation is performed using OPNET Modeler 14.5 to evaluate the proposed model. The results proved that the proposed model is highly scalable and achieves full network-bandwidth utilization in different situations based on different application requirements.

LINA: A Fair Link-Grained Inter-Datacenter Traffic Scheduling Method With Deadline Guarantee

Inter-datacenter networks take the role of connecting edge clusters and datacenters distributed globally. Nowadays, inter-datacenter network topologies are becoming more and more complicated with multiple links on each path, multiple paths between two datacenters, and overlaps between different paths. Hence, a link-grained scheduling method is necessary for making routing decisions and bandwidth allocation strategies when scheduling inter-datacenter transfers. For inter-datacenter transfers, deadline guarantee and fairness are essential and conflict requirements. Unfair preemptive scheduling methods ensure deadlines but cause starvation and service interference problems, while fairly sharing bandwidth among transfers ensures fairness but causes deadlines missed. However, a link-grained scheduling method simultaneously ensuring these two conflicting objectives is still missing. In this paper, a centralized controller, called LINA, is proposed to find fair link-grained bandwidth allocation strategies, which ensure transfers can compete fairly with each other for bandwidth on links and guarantee their deadlines in the meanwhile. First, we formulate the competition among transfers as a bottleneck routing game and prove the existence and non-uniqueness of its Nash Equilibrium, which is also the optimal bandwidth allocation strategy. Then, we propose a deep reinforcement learning based algorithm to derive it. Finally, experimental results show that LINA achieves superior performance than state-of-the-art methods in terms of fairness by 50%.

IMPROVEMENT OF THE ANALYTICAL METHOD FOR CALCULATING THE AVAILABLE THROUGHPUT WITH THE USE OF SIMULATION TOOLS

Background. The problem of this work is to optimize the currently used methods for calculating the available bandwidth based on analytical calculations by building a simulation model in the AnyLogic software package. Most scientists are working on the issues of laying the traffic schedule without taking into account the complex of issues related to predictive modeling and improving the accuracy of calculating the available capacity.&#x0D; Materials and/or methods: simulation modeling, economic and mathematical modeling, AnyLogic software package, methods of general systems theory, logistics.&#x0D; Results. In the course of the work, an abstract model of a railway line was developed and built that simulates the movement of trains according to the specified traffic parameters. The results of the study can be used in the dispatching centers of transportation management of JSC “Russian Railways” in order to optimize the analysis of the subject of the completed work of the railway landfill of the road.&#x0D; Conclusion. The paper considers and proposes a method for calculating the available capacity on a railway line based on existing analytical formulas and the use of simulation modeling tools in order to adjust them and calculate the value of the simulation modeling coefficient proposed in the work.

Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks

In non-terrestrial networks, where low Earth orbit satellites and user equipment move relative to each other, line-of-sight tracking and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous line-of-sight estimation and channel impairment compensation are crucial for user equipment to access a satellite and maintain connectivity. In this paper, we propose a framework based on actor-critic reinforcement learning for traffic scheduling in non-terrestrial networks scenario where the channel state is non-stationary due to the variability of the line of sight, which depends on the current satellite elevation. We deploy the framework as an agent in a multipath routing scheme where the user equipment can access more than one satellite simultaneously to improve link reliability and throughput. We investigate how the agent schedules traffic in multiple satellite links by adopting policies that are evaluated by an actor-critic reinforcement learning approach. The agent continuously trains its model based on variations in satellite elevation angles, handovers, and relative line-of-sight probabilities. We compare the agent’s retraining time with the satellite visibility intervals to investigate the effectiveness of the agent’s learning rate. We carry out performance analysis while considering the dense urban area of Paris, where high-rise buildings significantly affect the line of sight. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1times the satellite visibility time at given elevations, which guarantees efficient use of satellite visibility.

Создание автоматизированной программы по расчету значений целевых показателей для выполнения графика движения

Purpose: Achievement of the autonomy of calculations of target indicators for the implementation of passenger and suburb train schedule, decision-making and operational management, based on obtained calculations. Writing the program "Calculation of Target Indicator Values for Traffic Schedule Implementation" is due to the need to improve process implementation quality and to obtain maximal efficiency of planning and management system in a whole. Methods: Pascal programming language was used to automate the calculation of target indicators. Results: Creation and writing of independent software product with the preservation of organizational and mathematical methods of the work with information for to make managerial decisions. Practical significance: Automated calculation of target indicators for passenger and suburb train traffic schedule implementation will allow passenger complex all participants to consider the automation of the group of the processes related to information collection and processing on the process implementation "Train Traffic Schedule Implementation" through a single window. The proposed software product can be recommended for practical usage in the passenger complex of JSC "Russian Railways".

Ship traffic optimization method for solving the approach channel and lock co-scheduling problem of the Three Gorges Dam on the Yangzi River

Since the demands of waterway transport on the Yangzi River have been growing in recent years, traffic congestion at Three Gorges Dam (TGD) has become a very serious issue, which leads to long waiting time as well as environmental pollution. To improve the efficiency of the ship traffic scheduling, this paper investigates an approach channel and lock co-scheduling problem of the TGD on the Yangzi River. A new mathematical model is presented to optimize the lock chamber utilization rate, average waiting time and total energy consumption of ships, simultaneously. To solve this problem, a multi-objective metaheuristic algorithm (MOMA) based on fuzzy correlation entropy is proposed. In the algorithm, with a ship sequence encoding scheme, a pre-grouping panning approach is firstly designed for decoding. Then, a fitness evaluation mechanism using fuzzy correlation entropy is adopted to assess the solutions. Finally, an adaptive local reinforcement search strategy is introduced to improve local intensification ability. Various simulation experiments are carried out based on the real historical data at TGD. Computational results demonstrate the applicability and effectiveness of proposed MOMA. The result of this study is very useful for TGD to improve the ship traffic efficiency and save energy.

Опыт и перспективы автоматизации управления перевозочным процессом скоростного транспорта городских агломераций

The article analyses the technological process of transportation process organization and its control in different systems of urban agglomeration rapid-transit transport. It is presented the generalization of organization technological schemes of transportation process at the compilation of normative documents-schedules of: train traffic, rolling stock turnover, work of locomotive teams. Common features, allowing to share automation and digitization positive experience from one transport systems to another, are revealed. As a typical example for urban agglomeration rapid-transit transport, the work of Moscow Central Ring in data flow diagram notation is considered. It has been shown that the conditions of traffic planning on Moscow Central Ring are analogous to active ones on the subway ring lines. As generalizing notions, there are: mixing, non-parallelism, zoning, non-autonomy. Corresponding illustrative examples are given. Congested experience in the sphere of control automation for transportation process of rapid-transit transport of urban agglomeration is considered on the examples of railway section Nizhniy Novgorod – Uren’ as well as Kaluzhsko-Rizhskaya line of Moscow subway (electrodepots “Kaluzhskoye” and “Sviblovo”). The article describes initial data sets for to perform train traffic schedule, the purpose of its performance has been formulated, limitations, reflecting the links between objects, inside the set of given resources, and limitations, being defined by rules of passenger service, have been revealed. Analysis, pursued in the article, has shown the perspective directions of automated transport systems development on knowledge accumulated bases. As a result of the application of complex approach to the solution of automated control tasks at the use of artificial intelligence technologies and big databases usage, it’s planned to increase the efficiency usage for given resources set, train traffic schedule implementation percentage and others; to reduce information transfer error number as well as those, appeared as a result of negative human factor influence and so on.

Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks

With the proliferation of low-Earth-orbit (LEO) satellites, existing satellite networks need to be enhanced to better handle time-sensitive flows (TSFs). However, the migration of time-sensitive network (TSN) technology to satellite networks is challenged by the large space–time range and limited on-board resources, particularly in providing differentiated quality-of-service guarantees for multilevel TSFs. To address this issue, we propose a multilevel queue-based TSN technique that uses latency requirements as an indicator for traffic scheduling. This approach improves the time-sensitive transmission capability of services with different requirements in LEO satellite networks. We conducted a simulation evaluation under a Walker constellation, and the results demonstrate that our proposed method could significantly improve network throughput, and reduce the packet loss ratio by 90% and the time-out ratio by 14.5%. Additionally, our proposed mechanism could accommodate more TSFs with acceptable latency requirements.

Highly Efficient Traffic Planning for Autonomous Vehicles to Cross Intersections Without a Stop

Waiting in a long queue at traffic lights not only wastes valuable time but also pollutes the environment. With the advances in autonomous vehicles and 5G networks, the previous jamming scenarios at intersections may be turned into non-stop weaving traffic flows. Toward this vision, we propose a highly efficient traffic planning system, namely DASHX, which enables connected autonomous vehicles to cross multi-way intersections without a stop. Specifically, DASHX has a comprehensive model to represent intersections and vehicle status. It can constantly process large volumes of vehicle information, resolve scheduling conflicts, and generate optimal travel plans for all vehicles coming toward the intersection in real time. Unlike existing works that are limited to certain types of intersections and lack considerations of practicability, DASHX is universal for any type of 3D intersection and yields the near-maximum throughput while still ensuring riding comfort. To better evaluate the effectiveness of traffic scheduling systems in real-world scenarios, we developed a sophisticated open source 3D traffic simulation platform (DASHX-SIM) that can handle complicated 3D road layouts and simulate vehicles’ networking and decision-making processes. We have conducted extensive experiments, and the experimental results demonstrate the practicality, effectiveness, and efficiency of the DASHX system and the simulator.

Flexible Cyclic Queuing and Forwarding for Time-Sensitive Software-Defined Networks

Time-Sensitive Networking (TSN) is emerging to support critical real-time applications in Industry 4.0. Recent proposals leverage Cyclic Queuing and Forwarding (CQF) to achieve bounded-delay transmission for cyclic flows in TSN. However, the CQF is not flexible enough in two aspects. First, it cannot achieve zero jitter. The Ping-Pong queue-based model in CQF will introduce the jitter of two cycles, which is inapplicable to industrial automation scenarios where isochronous flows require zero jitter. Second, it may require setting the maximum queue length to a fixed value in advance and scheduling the flows offline, which is challenging for dynamic traffic scheduling. In this paper, we firstly present a time-aware cyclic-queuing (TACQ) mechanism to enable zero jitter for CQF. TACQ consists of a novel no-wait shaper (NWS) and a cyclic-queuing shaper (CQS). The NWS handles isochronous flows by strictly limiting the transmission time of flows that do not overlap on each output port and each period. The CQS is extended from CQF to schedule cyclic flows. Then, we propose a variable time slot mechanism and a novel incremental routing and scheduling (IRAS) algorithm based on software-defined networking (SDN) to online schedule dynamic flows. Simulation results show that TACQ significantly reduces the delay of isochronous flows and achieves zero jitter compared with CQF. And the IRAS algorithm approaches 96.1% of the optimal solution in scheduling 2000 flows with a feasible per-flow computational time.

Joint Routing and Scheduling With Cyclic Queuing and Forwarding for Time-Sensitive Networks

Various Industrial Internet of Things (IIoT) devices and their compelling applications have witnessed exponential growth over the past few years. Compared to the consumer internet, the industrial IoT often faces tough communication latency boundaries and low packet loss. Recently, time-sensitive networks (TSN) technique has been considered the most promising solution to tackle these issues, providing deterministic communication guarantees for time-sensitive applications. In TSN, to achieve bounded low latency, it introduces the Cyclic Queuing and Forwarding (CQF) mechanism to shape and regulate the transmission selection to provide the protected transmission window for time-triggered (TT) flows. Currently, a considerable amount of literature has explored traffic scheduling with the CQF mechanism in TSN. However, these methods ignore the impact of routing policy on scheduling optimization. In this paper, we design two algorithms to tackle the problem of joint routing and scheduling optimization with the CQF mechanism for TSN. We propose a TT flow sorting algorithm (JDPS) to measure the scheduling priority weight of each TT flow, which uses the normalization method to calculate the weight based on the flow's deadline, period, and size. Then, we propose a joint routing and scheduling optimization with load balance algorithm (JRS-LB) to improve the scheduling success radio and the network resource utilization, which jointly considers the injection time slot offset, scheduling, and routing of TT traffic. The evaluation results mainly focus on the scheduling success ratio, execution time, and resource utilization for the proposed algorithm compared to the Tabu and Naive algorithms. Extensive simulations indicate that the proposed JDPS and JRS-LB algorithms can improve the scheduling success radio compared with the other algorithms.

Design and implementation of the food supply chain traceability system based on the blockchain

Traditional enterprise-level food supply chain traceability systems are notoriously expensive and complex to set up, but there is a solution that can successfully address both of these problems: the food supply chain traceability system. This study, on the other hand, suggests a blockchain-based food supply chain traceability system business system. The paper is concerned with the food supply chain traceability system. The authentication and authorization procedures, traffic scheduling, and settlement monitoring are examples of some of the operations that make use of the blockchain infrastructure. In the meanwhile, the throughput of the system is increased by making improvements to the consensus method. Users of this system can provide customized service needs for food supply chain traceability systems by using this system. The issue of locking up the system in the food supply chain traceability system is successfully solved by the system, which embodies a great deal of application potential inside the food supply chain traceability system.