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In today's data–driven world, the explosive growth of network traffic often leads to network congestion, which seriously affects service performance and user experience. Network traffic scheduling is one of the key technologies to deal with congestion problems. Traditional traffic scheduling methods often rely on static rules or pre–defined policies, which make it difficult to cope with dynamically changing network traffic patterns. Additionally, the inability to efficiently manage tail contributors that disproportionately contribute to traffic can further exacerbate congestion issues. In this paper, we propose ESTS, an efficient and secure traffic scheduling based on private sketch, capable of identifying tail contributors to adjust routing and prevent congestion. The key idea is to develop a randomized admission ( R A ) structure, linking two count–mean–min (CMM) sketches. The first CMM sketch records cold items, while the second, following the R A structure, stores hot items with high frequency. Moreover, considering that tail contributors may leak private information, we incorporate Gaussian noise uniformly into the CMM sketch and R A structure. Experimental evaluations on real and synthetic datasets demonstrate that ESTS significantly improves the accuracy of feature distribution estimation and privacy preservation. Compared to baseline methods, the ESTS framework achieves a 25% reduction in average relative error and a 30% improvement in tail contributor identification accuracy. These results underline the framework's efficiency and reliability. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The expansion of megaconstellation networks (MCNs) represents a promising solution for achieving global Internet coverage. To meet the growing demand for satellite services, multipath routing allows the simultaneous establishment of multiple transmission paths, enabling the transmission of flows in parallel. Nevertheless, the mobility of satellites and time-varying link states presents a challenge for the discovery of optimal paths and traffic scheduling in multipath routing. Given the inflexibility of traditional static deep reinforcement learning (DRL)-based routing algorithms in dealing with time-varying constellation topologies, DRL-based multipath routing (DMR) enabled by a graph neural network (GNN) is proposed as a means of enhancing the transmission performance of MCNs. DMR decouples the stochastic optimization problem of multipath routing under traffic and bandwidth constraints into two subproblems: multipath routing discovery and multipath traffic scheduling. Firstly, the minimum hop count-based multipath route discovery algorithm (MHMRD) is proposed for the computation of multiple available paths between all source and destination nodes. Secondly, the GNN-based multipath traffic scheduling scheme (GMTS) is proposed as a means of dynamically scheduling the traffic on each available path for each data stream, based on the state information of ISLs and traffic demand. Simulation results demonstrate that the proposed scheme can be scaled to constellations with different configurations without the necessity for repeated training and enhance the throughput, completion ratio, and delay by 42.64%, 17.39%, and 3.66% in comparison with the shortest path first algorithm (SPF), respectively. [ABSTRACT FROM AUTHOR]

With the rapid integration and development of industrial control and information networks, time-sensitive networks based on ethernet have received widespread attention.In these networks, time-triggered communication scheduling is adopted to ensure the determinacy of data transmission.However, existing scheduling algorithms have struggled to rapidly calculate the scheduling tables for large-scale periodic traffic.Therefore, a heuristic scheduling algorithm based on the earliest deadline with weight first-maximum transfer time first was designed.This algorithm, by flexibly adapting traffic sorting weights and enhancing backtracking methods, could resolve the scheduling issues of large-scale traffic in industrial automation scenarios in a relatively short time.Experimental results show that compared to the classic integer linear programming method, the earliest deadline with weight first-maximum transfer time first heuristic scheduling algorithm is able to calculate a more optimal solution for the scheduling table of large-scale periodic traffic more swiftly.

In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to improve network resource utilization, optimize network performance, and adapt to the traffic scheduling requirements in a dynamic environment. This paper proposes a fine-grained traffic scheduling scheme based on multi-agent deep reinforcement learning (MAFS). This approach utilizes In-Band Network Telemetry to collect real-time network states on the programmable data plane, establishes the mapping relationship between real-time network state information and the forwarding efficiency on the control plane, and designs a multi-agent deep reinforcement learning algorithm to calculate the optimal routing strategy under the current network state. The experimental results demonstrate that compared to other traffic scheduling methods, MAFS can effectively enhance network throughput. It achieves a 1.2× better average throughput and achieves a 1.4–1.7× lower packet loss rate. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at large-scale time-sensitive networking (TSN) scenarios that can only ensure frequency synchronization between devices, a hybrid traffic scheduling mechanism was proposed based on time-aware shaper (TAS) combined with cycle specified queuing and forwarding (CSQF).Firstly, a scheme was investigated for achieving periodic cyclic mapping alignment between two adjacent nodes located in different time domains.Secondly, combining segment routing technology, a heuristic algorithm based on CSQF mechanism for joint routing and scheduling was proposed to complete resource allocation for large bandwidth traffic.The experimental results show that the proposed scheme significantly improves the system resource utilization and scheduling success rate compared to existing research results, achieving efficient TSN traffic scheduling in wide area network scenarios.

To solve the problem of huge pressure on network operation caused by the surge of 5G mobile data traffic and the continuous emergence of new network applications, a traffic scheduling system of the 5G core network user plane was designed and implemented, including network state information perception subsystem and routing decision subsystem.In the network state information perception subsystem, traditional in-band network telemetry methods incurred high bandwidth overhead and were not specifically designed for wireless network systems.Furthermore, the application of in-band network telemetry in the 5G core network faced challenges such as low measurement accuracy and the inability to guarantee QoS.A 5G core network user plane state information perception scheme based on in-band network telemetry was proposed.The telemetry information was inserted into the extended header of GTP-U message realize the measurement of network state along the path.The routing decision subsystem implemented the traffic scheduling algorithm based on the improved ant colony algorithm.The update method of the pheromone function was promoted by the perceived status information to complete the routing decision based on the real-time network state.The deployment test results showed that the network state information perception subsystem can perceive network information normally, and the routing decisions were superior to traditional routing algorithms in terms of delay, throughput and packet loss rate.

The space-air-ground integrated stereo communication networks can meet the communication requirements of wide coverage and massive connectivity of Power IoT （Internet of things）. However， the multi-layer heterogeneous communication networks are characterized by complex structure， decentralized nodes， and difficult operation and maintenance. Based on the concept of centralized control logic of SDN （software-defined networking）， a collaborative control scheme for stereo-heterogeneous communication networks in power IoT based on the collaborative orchestration system is proposed. The design requirements analysis of the collaborative orchestration system is carried out， and its functional design is completed. Given the core functional requirements of the system， an improved traffic scheduling method based on Q-learning is proposed. Simulation results show that the proposed method can improve the traffic throughput capacity and load balancing capacity of the heterogeneous communication networks.

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.

Abstract The new intelligent factory introduces Time-Sensitive Network into industrial Ethernet to provide real time and deterministic guarantee communication for production system. Since the problem pertaining to data leakage or damage during transmission has increasingly become pronounced, security protection technology has been introduced, but this technology will bring about a delay in user response and a decline in the quality of service. Meanwhile, ensuring the deterministic mixed transmission of time-sensitive and large-bandwidth data traffic supported by the same switching device is a still challenging problem. Therefore, this study proposes a hybrid security scheduling scheme which combines Time-Aware Shaper and cycle specified queuing and forwarding (CSQF). Specifically, the mechanism first adopts various encryption methods for different traffic, and afterward, it reduces its resource occupation by adjusting the sampling period of the time-sensitive traffic. At the same time, it adopts CSQF to schedule the large-bandwidth data traffic, thereby improving the scheduling success rate. According to the experimental results, this scheme enhances network security and network scheduling success rate by up to 51%. The scheduling of mixed traffic in the Time-sensitive Network is realized securely and efficiently.

The new intelligent factory introduces Time-Sensitive Network into industrial Ethernet to provide real time and deterministic guarantee communication for production system. Since the problem pertaining to data leakage or damage during transmission has increasingly become pronounced, security protection technology has been introduced, but this technology will bring about a delay in user response and a decline in the quality of service. Meanwhile, ensuring the deterministic mixed transmission of time-sensitive and large-bandwidth data traffic supported by the same switching device is a still challenging problem. Therefore, this study proposes a hybrid security scheduling scheme which combines Time-Aware Shaper and cycle specified queuing and forwarding (CSQF). Specifically, the mechanism first adopts various encryption methods for different traffic, and afterward, it reduces its resource occupation by adjusting the sampling period of the time-sensitive traffic. At the same time, it adopts CSQF to schedule the large-bandwidth data traffic, thereby improving the scheduling success rate. According to the experimental results, this scheme enhances network security and network scheduling success rate by up to 51%. The scheduling of mixed traffic in the Time-sensitive Network is realized securely and efficiently. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are terminal devices with different processing rates and different types of communication networks in the Internet of Things. Most of the transmitted data have certain requirements for delay and bandwidth. How to ensure effective data transmission between communication devices in the Internet of Things, make the data flow on each link meet the requirements and prevent data overflow is of great significance to control the flow in the Internet of Things. In this paper, the traffic scheduling with multiple QoS constraints is converted as a multiple objective optimization. The mathematical model of multiple objective optimization is solved by a multiple objective particle swarm optimization algorithm to obtain Pareto optimal solution. The experimental results on Mininet show that the proposed traffic scheduling is superior to previous ones. [ABSTRACT FROM AUTHOR]

Currently, network traffic scheduling strategy cannot be intelligent and on-demand, especially in the congestion caused by sudden network failures and escort scenarios of high-value services.They cannot guarantee latency-sensitive service experience on demand.The delay-sensitive attribute requirements of different network traffic were analyzed and studied, and the internal correlation between the behavior characteristics of varying network traffic and its delay sensitivity requirements was explored.Then, AI technology was used to learn this inherent relationship and construct its mapping relationship, realizing a traffic scheduling technical solution based on the intelligent awareness of delay-sensitive traffic.At the same time, considering the practical issues of interpretability and deploy-ability of AI models, reinforcement learning (RL) technology was used to prune and optimize the interpretable decision tree model, which improved the robustness of the model and made model lighter and easier to implement in equipment deployment.Through experiments by the collected real network traffic, the decision tree model optimized by reinforcement learning could improve the awareness accuracy by 1.75% in a single inference case, and the inference performance was improved by about 30%.The experiment also proved that using micro-statistical features for multiple inferences could help improve the model accuracy; in all experiments, the scale of the decision tree model optimized by RL was reduced by about 60.0%～87.2%, and the Saras had better optimization performance than Q-learning.

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.

The upcoming sixth generation (6G) mobile networks require integration between terrestrial mobile networks and non-terrestrial networks (NTN) such as satellites and high altitude platforms (HAPs) to ensure wide and ubiquitous coverage, high connection density, reliable communications and high data rates. The main challenge in this integration is the requirement for line-of-sight (LOS) communication between the user equipment (UE) and the satellite. In this paper, we propose a framework based on actor-critic reinforcement learning and generative models for LOS estimation and traffic scheduling on multiple links connecting a user equipment to multiple satellites in 6G-NTN integrated networks. The agent learns to estimate the LOS probabilities of the available channels and schedules traffic on appropriate links to minimise end-to-end losses with minimal bandwidth. The learning process is modelled as a partially observable Markov decision process (POMDP), since the agent can only observe the state of the channels it has just accessed. As a result, the learning agent requires a longer convergence time compared to the satellite visibility period at a given satellite elevation angle. To counteract this slow convergence, we use generative models to transform a POMDP into a fully observable Markov decision process (FOMDP). We use generative adversarial networks (GANs) and variational autoencoders (VAEs) to generate synthetic channel states of the channels that are not selected by the agent during the learning process, allowing the agent to have complete knowledge of all channels, including those that are not accessed, thus speeding up the learning process. The simulation results show that our framework enables the agent to converge in a short time and transmit with an optimal policy for most of the satellite visibility period, which significantly reduces end-to-end losses and saves bandwidth. We also show that it is possible to train generative models in real time without requiring prior knowledge of the channel models and without slowing down the learning process or affecting the accuracy of the models.

This work utilizes the transit network, which aims to combine the genetic algorithm for analyzing the theory and traffic scheduling based on the traditional methodology. The dynamic methodology is used to schedule the model of transit system, which aims to optimize the demand in the transit network. This model illustrates the methodology of the genetic based transit network (GATN) algorithm to enhance the primary challenges in the transit network. The proposed methodology provides to be significant, with minimizing the objective model of around 27.2%. The model significantly managed to lower the total routes available in the transit network and all travelers related to the time and the transit trip from the initial stage. The significant system obtained using the optimization methodology has 180 routes, 110 less than the initial network, which has a variation by different transit network. This final transmission has been minimized to 33.6% by the proposed methodology in the transit network length and 4.1% reduction in the transfer average. The transition obtained from the multi-level objective function to unique optimization that considers the weighted function proved to be effective.

Most of the existing studies on the scheduling of mixed traffic in time-sensitive network (TSN) focus on how to ensure low-latency of stream reservation (SR) traffic.However, SR traffic blocks best-effort (BE) traffic for a long time will lead to excessive delay in BE traffic, which is not conducive to the state maintenance of network and the management of system.In order to reduce the maximum end-to-end delay of BE messages, a traffic scheduling algorithm which combines with enqueue shaping was proposed.It reduced the blocking of BE traffic by reducing the reserved bandwidth of SR traffic.The algorithm first added enqueue buffers in front of SR queues.Then, the reserved bandwidth ratio among the same priority traffic was adjusted by setting the number of frames in each enqueue buffer entering the SR queue during enqueue shaping.Finally, it jointed egress shaping to reserve bandwidth resources for each SR flow to match its latency requirement.Simulation results showed that combining enqueue shaping can reduce the maximum end-to-end delay of BE messages by 9.66%～75.76%.

To solve the problem of uncertain end-to-end transmission delay caused by the separation of time-sensitive traffic scheduling and routing in time-sensitive network, a multi-constrained traffic scheduling method with joint routing planning was proposed.The inter-flow conflict degree of time-sensitive traffic was introduced to flexibly allocated traffic routing.On that basis, a scheduling constraint model of time-aware shaping and joint conflict-free routing was established to strictly guarantee the time-sensitive traffic scheduling with bounded low latency in both spatial and temporal dimensions.Experimental results show that the proposed method has good delay performance in different topological scenarios, and effectively reduces the delay caused by congestion conflicts under different time-sensitive traffic scales while ensuring link load balancing.

Developments presented in this paper are devoted to the dynalic scheduling of railway transport systems. In this context, our study deals with the implementation of cooperative methodologies to solve railway scheduling problems with predictive or unpredictable demands. Stochastic P-time Petri Nets (SP-TPN) are used for modelling. A new scheduling method based on genetic algorithms and allowing the insertion of predicted jobs is presented, in order to improve a number of traffic criteria such as travel time, total cost and maintenance activities. The aim of the proposed insertion method is to integrate the forecasted demand operations in the availabilities of the rolling stock, in order to minimize the inactivity periods and to increase the traffic rate. Finally, to illustrate the effectiveness and accuracy of the insertion approach, an application to a Sahel Railway networks is outlined.

In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to improve network resource utilization, optimize network performance, and adapt to the traffic scheduling requirements in a dynamic environment. This paper proposes a fine-grained traffic scheduling scheme based on multi-agent deep reinforcement learning (MAFS). This approach utilizes In-Band Network Telemetry to collect real-time network states on the programmable data plane, establishes the mapping relationship between real-time network state information and the forwarding efficiency on the control plane, and designs a multi-agent deep reinforcement learning algorithm to calculate the optimal routing strategy under the current network state. The experimental results demonstrate that compared to other traffic scheduling methods, MAFS can effectively enhance network throughput. It achieves a 1.2× better average throughput and achieves a 1.4–1.7× lower packet loss rate.

The software defined network separates the control plane from the data plane to achieve flexible traffic scheduling, which can use network resources more efficiently.However, with the increase of the number of flow entries, load rate, the number of connected hosts, and other factors, the forwarding efficiency of the SDN switch will be reduced, which will affect the end-to-end transmission delay.To solve the above problems, the forwarding efficiency aware traffic scheduling algorithm based on deep reinforcement learning was proposed.First, the switch state was integrated into the perception model, and the mapping relationship between switch state information and forwarding efficiency was established based on neural network.Then, combined with network state and traffic information, traffic scheduling policy was generated through deep reinforcement learning.Finally, the expert samples generated by the shortest path and load balance algorithms could guide the model training, which enabled the model to learn knowledge from expert samples to improve performance and accelerated the training process.The experimental results show that the proposed algorithm not only reduces the average end-to-end transmission delay by 15.31%, but also ensures the overall load balance of the network, compared with other algorithms.

Currently, network traffic scheduling strategy cannot be intelligent and on-demand, especially in the congestion caused by sudden network failures and escort scenarios of high-value services. They cannot guarantee latency-sensitive service experience on demand. The delay-sensitive attribute requirements of different network traffic were analyzed and studied, and the internal correlation between the behavior characteristics of varying network traffic and its delay sensitivity requirements was explored. Then, AI technology was used to learn this inherent relationship and construct its mapping relationship, realizing a traffic scheduling technical solution based on the intelligent awareness of delay-sensitive traffic. At the same time, considering the practical issues of interpretability and deploy-ability of AI models, reinforcement learning (RL) technology was used to prune and optimize the interpretable decision tree model, which improved the robustness of the model and made model lighter and easier to implement in equipment deployment. Through experiments by the collected real network traffic, the decision tree model optimized by reinforcement learning could improve the awareness accuracy by 1.75% in a single inference case, and the inference performance was improved by about 30%. The experiment also proved that using micro-statistical features for multiple inferences could help improve the model accuracy; in all experiments, the scale of the decision tree model optimized by RL was reduced by about 60.0%~87.2%, and the Saras had better optimization performance than Q-learning. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Nowadays, with the widespread application of the Internet of Things (IoT), mobile devices are renovating our lives. The data generated by mobile devices has reached a massive level. The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load. Mobile Edge Computing (MEC) has been proposed to solve these problems. Because of limited computation ability and battery capacity, tasks can be executed in the MEC server. However, how to schedule those tasks becomes a challenge, and is the main topic of this piece. In this paper, we design an efficient intelligent algorithm to jointly optimize energy cost and computing resource allocation in MEC. In view of the advantages of deep learning, we propose a Deep Learning-Based Traffic Scheduling Approach (DLTSA).We translate the scheduling problem into a classification problem. Evaluation demonstrates that our DLTSA approach can reduce energy cost and have better performance compared to traditional scheduling algorithms. [ABSTRACT FROM AUTHOR]

IDC customers reduce costs through traffic scheduling, which leads to waste of resources and increase of operation and maintenance costs of operators.Area billing could effectively improve this problem.It made a comparison between area billing and 95% peak rate billing method, and innovatively tried the step area algorithm.By connecting the difference between 95% peak rate value and average value and 95% peak rate billing and area billing price, the data dimension reduction was carried out, and the two-dimensional scatter diagram was obtained by programming exhaustive calculation, and the relationship between them was analyzed to explore the possibility of area billing.The experimental results show that the area of pricing could guide the customer does not adopt the way of traffic scheduling to reduce costs, in addition ladder area of billing way can meet the current traffic situation in the certain degree differ with 95 billing way smaller, the flow demand rises customers reduce costs, operators can get the corresponding profits at the same time, achieve a win-win situation.

Objectives: With the arrival of the industry 4.0 era, the industrial production control system is becoming more and more intelligent, which puts forward higher requirements for real-time and deterministic transmission of information. Time-Sensitive Networking (TSN) has been introduced into industrial network because of its good compatibility and low delay jitter. To realize efficient transmission of mixed traffic in industrial network with the help of TSN, we explored a new traffic scheduling mechanism in TSN. Methods: Based on architecture of the centralized software defined network (SDN), we designed a method to determine the minimum scheduling slot of the network and adjusted sampling period of Scheduled Traffic (ST) based on the minimum slot. By reducing occupation of transmission bandwidth by ST traffic, more transmission resources were reserved for Stream Reservation (SR) traffic to improve network schedulability. Furthermore, for SR traffic, a parity mapping scheme was proposed. When SR flow was not schedulable, a flow offset planning (FOP) algorithm was designed to offset injection time of SR flow, which can further improve schedulability of the network by improving utilization of system resources. Results: To verify performance of core algorithm of HSTC mechanism, we built an experimental platform and compared performance of our mechanism with existing mechanisms from the perspectives of ST traffic bandwidth occupation, traffic scheduling priority, SR traffic mapping, injection slot selection, etc. In the experiment, the maximum transmission unit (MTU) of network was 1500B, the maximum buffer size of single queue in switch was 6MTU, and link rate was 1000 Mbit/s. The maximum sampling period and packet length of each ST flow were randomly selected from the set {0.6, 0.8, 1, 1.2, 1.6}ms, {0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}kB. Minimum sampling period of ST flow was set to 0.1 ms. And the deadline (DDL) of ST flow was its actual sampling period. Sampling period and packet length of SR flow were randomly selected from set {4, 6, 8, 10, 12, 16, 20}ms, {1.5, 2, 2.5, 3, 3.5, 4, 4.5}kB. DDL of each SR flow was a random integer value within range of [0.5× T j R , T j R ]. Main experimental results are showed as follows: ①Adjusting sampling period of ST traffic: if sampling period was not adjusted, when the number of flows reached 12, transmitting ST traffic occupied more than 80% of network bandwidth. However, after adjusting sampling period by our scheme, the bandwidth occupation of ST traffic was greatly reduced. ②Traffic scheduling priority: weighted ranking scheme proposed in this paper had the best performance. Performance ranking of other schemes was the maximum packet length first, the shortest deadline first, and the minimum sampling period first. Compared with the sub optimal scheme, weighted ranking scheme can improve scheduling success rate by up to 0.52. ③SR traffic mapping: as the number of SR flows increased, impact of parity mapping scheme on improving scheduling success rate became more significant. The maximum difference between scheduling success rate of our scheme and the scheme which mapped according to DDL was 20%. ④Compared with the suboptimal random slot injection scheme, the slot sequencing scheme in HSTC can increase network scheduling success rate by up to 0.77, and the limit bandwidth utilization can reach 88%.⑤Overall performance comparison: we compared with two mechanisms which were proposed in two representative existing literatures under the same simulation parameter configuration. The experimental results showed that HSTC mechanism realized dual optimization of reducing solution complexity and improving scheduling performance. Conclusions: How to make full use of TSN's accurate flow scheduling capability to provide certainty and real-time guarantee for production control system is still a research focus of TSN. Therefore, we proposed a mixed traffic scheduling mechanism called HSTC, which combined two existing schemes of Time-Aware Shaper (TAS) and Cyclic Queuing and Forwarding (CQF) and formulated different scheduling strategies for time-sensitive traffic and large bandwidth traffic according to their characteristic. The experimental results showed that HSTC mechanism significantly improved network schedulability by improving system resource utilization, and it realized efficient scheduling of mixed traffic. Existing network schedule schemes for TSN are mostly based on off-line scheduling scenarios. However, in actual industrial network, there is still a small number of burst traffic triggered by events. Burst traffic has no fixed parameters but has an important impact on the normal operation of the system. Therefore, how to improve our current mechanism to support mixed transmission of burst traffic at the same time is our next research direction.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the host-centric TCP/IP network is hard to fulfill the new network requirements, the Information Center Networking (ICN) emerged. The communication mode of ICN increases the volatility and complexity of the traffic, so how to efficiently carry out traffic scheduling of this complex network has undoubtedly become the key and core problem in ICN. Delay and throughput are indispensable indicators to measure the network performance. However, there is no one to synchronously optimize and balance delay and throughput in the existing work. To solve this problem, based on the multi-objective genetic algorithm Non-dominated Sorting-based Algorithm II, we propose Density Sorting-Based Evolutive Algorithm (DSEA) to optimize the delay and throughput at the same time. The simulation results show that, compared with the existing multi-objective genetic algorithms, DSEA has better performance in evaluation indexes Generational Distance, Inverted Generational Distance, Hypervolume, and Pareto front. And it can reduce the delay by 30.01%, improve the throughput by 43.37% under the premise of a good balance between the two indicators. [ABSTRACT FROM AUTHOR]

PFC (Priority-based Flow Control), which is used to give full play to the performance advantages of RDMA, can cause problems such as head-of-line (HoL) blocking. PFC-based method cannot eliminate the congestion fundamentally. At the same time, another approach to deal with congestion in the data center networks, commonly known as "injection throttling" mechanism, may result in slower effective response due to the delay between congestion detection and feedback to the sources. In this article, we have optimized the two types of algorithms (DCI and NACC) and used them in combination (DCI-NACC). We propose a novel scheduling strategy of combining PFC-based algorithm and "injection throttling"-based algorithm using programmable data plane device for lossless data center networks. Specifically, we design a dynamic congestion isolation (DCI) method to optimize PFC. DCI ensures lossless transmission in the data center networks and eliminates the HoL blocking problem caused by PFC. We also propose a network-assisted congestion control (NACC) algorithm in which the feedback information is directly sent from the switch where congestion occurs in the network. NACC increases the efficiency of network congestion perception and reduces the delay that feedback take effect. Experimental results show that, compared with PFC and ECN algorithm, DCI-NACC algorithm has significant improvement in network performance (normalized throughput, network latency, and flow completion time). Especially, simulation experiments show that NACC can greatly reduce the number of times that PFC PAUSE is triggered. The simulation experiments confirm that DCI-NACC algorithm can effectively improve the performance of the data center networks on the basis of lossless transmission. [ABSTRACT FROM AUTHOR]

In large‐scale network mode, congestion may occur in any link at any time, so traffic scheduling becomes a research hotspot. This paper pays attention to the traffic scheduling method in information‐centric networking (ICN). ICN transforms the host‐oriented end‐to‐end communication mode in the traditional network architecture into an information‐centric network architecture, which uses the network in‐network caching to significantly improve the content distribution efficiency. This is applied to traffic scheduling through the introduction of artificial intelligence (AI). Therefore, this paper proposes a traffic scheduling strategy based on particle swarm optimization (PSO) algorithm in ICN. The experimental results demonstrate that the proposed traffic scheduling algorithm in this paper outperforms three benchmarks in terms of average latency, packet loss rate and average throughput. [ABSTRACT FROM AUTHOR]

Dispatching emergency vehicles (EVs) to fatal accidents or fires as fast as possible is vital to save lives; however, minimizing an EV’s travel time to the rescue spot is still an open challenge. This work presents a path planning and traffic clear-out scheduling scheme to lessen the EV’s travel time in the vision of the Internet of Vehicles (IoV). Initially, the system searches a list of candidate paths to the rescue spot with the estimated time of arrival (ETA) at the EVs’ maximum speed, regardless of the traffic conditions. After that, a vehicle clear-out process evaluates the delay time of clearing out the traffic obstacles on each path to identify the fastest driving path. Finally, the system estimates and issues the signal preemption schedules for the junctions of the selected route to coordinate the traffic flows and let the EV pass through smoothly. From the macro perspective, this work seeks to control the dynamic traffic proactively to reserve a lane for the EV – a feasible approach in the future of connected vehicles. This controllable model apparently contrasts with the conventional techniques of finding the least-cost paths with the uncertainty of traffic state prediction or traffic light preemption alone at the intersections. The simulation shows that our approach can outperform the state-of-the-art solutions in terms of the EV’s travel time reduction, particularly if the congestion or heavy load road segments appear on the selected route but far from the departure location of the EV. [ABSTRACT FROM AUTHOR]

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Advances in automotive technology require networks to support a variety of communication requirements, such as reliability, real-time performance, low jitter, and strict delay limits. Time-Sensitive Network (TSN) is a keyframe transmission delay-guaranteed solution based on the IEEE 802 architecture of the automotive Ethernet. However, most of the existing studies on automotive TSN performance are based on a single mechanism, lacking a complete and systematic research tool. At the same time, the design method should be considered from a global perspective when designing an automotive TSN system, rather than only considering a single mechanism that TSN applies to. This paper discusses the correspondence between traffic types and automotive scenarios and proposes a methodology to target the delay constraint of traffic types as the design goal of automotive TSN networks. To study the performance of automotive TSN under different mechanisms such as time-aware shaper (TAS), credit-based shaper (CBS), cyclic queuing and forwarding (CQF), etc., this paper also develops a systematic automotive TSN simulation system based on OMNeT++. The simulation system plays a crucial role in the whole methodology, including all applicable TSN standards for the automotive field. Lastly, a complex automotive scenario based on zonal architecture provided by a major motor company in Shanghai is analyzed in the simulated system; verifying TSN can guarantee real-time performance and reliability of the in-vehicle network. [ABSTRACT FROM AUTHOR]