Packet Scheduling Scheme Research Articles

Value of Information-Based Packet Scheduling Scheme for AUV-Assisted UASNs

Value of Information-Based Packet Scheduling Scheme for AUV-Assisted UASNs

Traffic Classification and Packet Scheduling Strategy with Deadline Constraints for Input-Queued Switches in Time-Sensitive Networking

Deterministic transmission technology is a core key technology that supports deterministic real-time transmission requirements for industrial control in Time-Sensitive Networking (TSN). It requires each network node to have a deterministic forwarding delay to ensure the real-time end-to-end transmission of critical traffic streams. Therefore, when forwarding data frames, the switch nodes must consider the time-limited requirements of the traffic. In the input-queued switch system, an algorithm for clock-synchronized deterministic network traffic classification scheduling (CSDN-TCS) is proposed to address the issue of whether a higher-quality-of-service (QoS) performance can be provided under packet deadline constraints. First, the scheduling problem of the switch is transformed into a decomposition problem of the traffic matrix. Secondly, the maximum weight-matching algorithm in graph theory is used to solve the matching results slot by slot. By fully utilizing the slot resources, as many packets as possible can be scheduled to be completed before the deadline arrives. For two types of packet scheduling problems, this paper uses the maximum flow algorithm with upper- and lower-bound constraints to move packets from a larger deadline set to idle slots in a smaller deadline set, enabling early transmission, reducing the average packet delay, and increasing system throughput. When there are three or more types of deadlines in the scheduling set, this scheduling problem is an NP-hard problem. We solve this problem by polling the two types of scheduling algorithms. In this paper, simulation experiments based on the switching size and line load are designed, and the Earliest Deadline First (EDF) algorithm and the Flow-Based Iterative Packet Scheduling (FIPS) algorithm are compared with the CSDN-TCS algorithm. The simulation results show that under the same conditions, the CSDN-TCS algorithm proposed in this paper outperforms the other two algorithms in terms of success rate, packet loss rate, average delay and throughput rate. Compared with the FIPS algorithm, the CSDN-TCS algorithm has lower time complexity under the same QoS performance.

On the design and performance of scheduling policies exploiting spatial diversity for URLLC

In this paper, we study the performance of packet scheduling schemes for Ultra-Reliable Low-Latency Communications (URLLC) services. We exploit spatial diversity (i.e., redundant coverage of users) guaranteed in numerous 5G radio access network scenarios to examine the impact of multi-connectivity. Thus, we consider a set of URLLC users connected to two frequency layers or Radio Access Technologies (RATs) to ensure minimal queuing time. We review four packet scheduling and redundancy schemes, namely Join-the-Shortest-Queue (JSQ), the shortest expected delay (SED), systematic Redundancy (RED), and redundancy with Cancellation upon completion (CAN). We choose the outage probability as a metric, defined as the packet’s probability of arriving after some given target delay. We show that RED performs well at low load, whereas JSQ and SED are better when the load rises. Besides, CAN outperforms all other schemes. We then discuss the trade-off between performance and implementation complexity.

AN ENHANCED RESOURCE ALLOCATION SCHEME FOR LONG TERM EVOLUTION ADVANCED NETWORK

The emerging Long-Term Evolution Advanced (LTE-A) is an extended version of Long-Term Evolution (LTE) fourth-generation (4G) wireless broadband technologies that supports high speed transmission of multimedia applications, like video streaming, multimedia online game, Voice over IP (VoIP), browsing over the web etc. The Third Generation Partnership Project (3GPP) does not define a standard packet scheduling strategy in LTE-A. Therefore, it is expected for researchers and network service providers to design efficient schemes. This paper proposed an Enhanced Resource Allocation Scheme (E-RAS) that improves the packet scheduling strategy of LTE-A by addressing issues related to delay and fairness of users with both high and low channel conditions, for both Real Time (RT) and Non-Real Time (NRT) flows. The aim was achieved by using a fair spectrum allocation mechanism that consider the priority of RT flows of cell-centre users, without neglecting the throughput requirements of edge-centre users. Moreover, the proposed algorithm introduces an efficient traffic load balancing mechanism to efficiently regulate traffic load in LTE-A network. To evaluate the proposed scheme, simulation was conducted using the MATLAB programming with LTE-A system level simulator. The result obtained shows that the proposed scheme achieves better delay for RT and NRT traffics by 7% and 11% respectively as against the Resource Allocation Scheme to Optimize the Throughput (RASOT) scheme.

Deadline-Aware Scheduling for Transmitted RSU Packets in Cooperative Vehicle-Infrastructure Systems

In Cooperative Vehicle Infrastructure System (CVIS), the roadside unit (RSU) obtains many kinds of monitoring data through observation equipment carried by the RSU. The monitoring data from RSUs are transmitted to an RSU that is connected to the backbone network using the “store–carry–forward” scheme through the mobile vehicle. The monitoring data obtained by RSUs are timely, and different types of monitoring data have corresponding timelines. Reducing end-to-end delays to ensure more packets can be transmitted before deadlines is challenging. In this paper, we propose a Distributed Packet Scheduling Scheme for Delay-Packets Queue Length Tradeoff System (DDPS) in CVIS to solve the multi-RSU-distributed packet transmission problem. We also establish the vehicle speed state, vehicle communication quantity prediction, data arrival, and end-to-end delay minimization models. After Lyapunov’s optimization theory transformed the optimization model, a knapsack problem was described. The simulation results verified that DDPS reduced the end-to-end average delay and ensured the data queue’s stability under packet deadline conditions.

Design, implementation, and performance evaluation of an earliest-deadline-first packet scheduling scheme in P4 hardware switches

Real-time applications such as video conferencing, virtual reality (VR), high-speed train controls, nuclear plant controls, etc. impose stringent limitations on the maximum end-to-end latency that their packets can tolerate. In such applications, if a packet arrives at its destination node but misses its deadline, it will be useless. On a network, a packet often needs to pass multiple switches on its way from its source node to its destination node. In some of these switches, the packet may experience large queuing delays and the accumulation of these delays may cause the packet to miss its deadline at its destination node. In this work, we design and implement an earliest-deadline-first (EDF) packet scheduling scheme in P4 hardware switches to overcome this problem. We implement two versions of our scheme that use two queues and six queues respectively for real-time packets to study how the number of queues may affect the performance of our scheme. We evaluate the performance of our scheme under different levels of congestion conditions. Experimental results show that, compared with the baseline scheme that dispatches all real-time packets to the same high-priority queue, our scheme greatly reduces the deadline miss rate of real-time packets when the real-time traffic ratio in the network is between 40% and 80%.

FAIFO: UAV-assisted IoT programmable packet scheduling considering freshness

UAVs have the qualities of large coverage, flexible deployment, scalability, low cost and easy programming. Myriad UAVs are flexibly deployed in the air, on the ground and even on submarines, enabling ubiquitous connectivity. Thus, UAV-assisted IoT is considered a potential solution to support time-sensitive applications in sixth-generation (6G) networks. Such as the Industrial Internet of Things (IIoT), smart homes, smart cities and self-driving vehicles. A large amount of sensor data will be collected and carried by drones for transmission to destination nodes for further processing and analysis. Unlike traditional broadband services, most IoT applications transmit short and time-varying data packets carrying sensor updates. Many studies on time-sensitive applications at this stage still focus on optimization of latency, reliability and other metrics. Less attention has been paid to the freshness of the updates and the timeliness of the data. Age of Information (AoI) is a new metric introduced for time-sensitive applications in recent years. Unlike latency, which represents real-time, AoI utilizes the concept of freshness to depict the timeliness of data. The larger the AoI, the less fresh and timely the information is. To better serve time-sensitive applications, we cannot reduce the stringent requirements for latency while ensuring data timeliness. Therefore, we propose Freshness-based Admission in First Out (FAIFO), a novel fine-grained programmable packet scheduling scheme. FAIFO can guarantee the freshness of information for time-sensitive applications while meeting their low-latency requirements. FAIFO first uses the concept of admission control to rapidly filter high-priority (low-latency) packets into queue A to meet the real-time requirements of time-sensitive applications. Subsequently, depending on the freshness level of the packets, FAIFO will decide whether the discarded packets need to be cached in queue B to meet the timeliness requirements of time-sensitive applications. Finally, we improve the credit-based shaper (CBS) algorithm to perform traffic shaping on packets in queue A and queue B to ensure that high-priority queue A is forwarded first and freshness queue B is forwarded in a timely manner. Simulation experiments compare FAIFO with the ideal benchmark Push-In-First-Out (PIFO), state-of-the-art SP-PIFO, and AIFO. The results indicate that our FAIFO can also obtain the approximate PIFO effect, and the delay guarantee is comparable to SP-PIFO and AIFO. More importantly, FAIFO’s guarantee of information freshness is significantly better than other compared algorithms.

QoS Aware Uplink Scheduling for M2M Communication in LTE/LTE-A Network: A Game Theoretic Approach

M2M communication in the LTE network is gaining attention with a growing number of connected devices and adaptation of new emerging technologies. Usually, the traffic generated by M2M devices is heterogeneous in terms of packet size, intensity, strict delay, and throughput requirements. The M2M traffic generally flows in the uplink direction. It imposes several challenges in designing scheduling for uplink in the LTE network. The research community proposed various solutions regarding the QoS handling in M2M communications. Allocating the resources based on the QoS of machine type communications (MTCs) has a challenge in deciding on devices’ priority. This work applies game theory to control devices’ strategic behavior claiming a false priority. In this paper, a Quality-of-Service aware uplink packet scheduling scheme is proposed using a combinatorial game. The packet scheduling problem is modeled as an Auction game that handles the scheduling in both Time-Domain and Frequency-Domain. The packet scheduler uses the QoS requirement as an allocation metric, and a power control mechanism is applied to control the devices’ strategic behavior. Simulation is performed in MATLAB R2019b. The performance of the proposed schemes is evaluated in terms of the total system utility in QoS satisfaction against standard schedulers like Round Robin and Proportional Fair schedulers and some other schedulers proposed in the literature.

Packet Scheduling for Internet of Remote Things (IoRT) Devices in Next Generation Satellite Networks

Background: The massive amount of deployment of Internet of Things (IoT) devices via wireless communications has presented a new paradigm in next-generation mobile networks. The rapid growth in the deployment of IoT devices can be linked to the diverse use of several IoT applications for home automation, smart systems, and other forms of innovations in businesses and industry 4.0. Methods: There is a need for a robust network infrastructure to actualize the huge traffic demand of IoT communications in this new paradigm across the globe, including rural and remote areas. However, due to technical and economic constraints, the terrestrial network infrastructure is not able to fulfill this requirement. Hence, the need for satellite network infrastructure. This solution will be of immense benefit to the provision of remote health care, disaster management, remote sensing, and asset tracking, and environmental monitoring, to name a few. While this remains an interesting solution, packet scheduling, which is one of the key radio resource management functions, is still a challenging issue that remains undefined, especially in a satellite network scenario that has its peculiarities and challenges. Results: Hence, the goal of this research work is to design a new packet scheduling scheme suitable for machine-type communications and also mixed-use case scenarios in satellite network scenarios. The performance evaluation of the proposed packet scheduler is conducted through simulations. Conclusion: The newly proposed packet scheduling scheme provides an improvement of approximately 7 Mbps and 0.5 bps/Hz in terms of throughput and spectral efficiency performances, respectively, in mixed-use case scenarios, when compared to known throughput optimal packet schedulers, without serious compromise to other performance metrics.

A Spatiotemporal Model for Hard-Deadline Multistream Traffic in Uplink IoT Networks

Ultrareliable and delay-intolerant message delivery is one of the key components for Internet of Things (IoT) and cyber–physical systems to support real-time control and real-time interaction. In this article, we provide a spatiotemporal framework that captures the packet loss rate (PLR) for large-scale grant-free uplink IoT networks with multiple types of hard-deadline traffic. An independent and a prioritized packet scheduling schemes are proposed and investigated for efficiently realizing frequency diversity. Tools from stochastic geometry and queuing theory are utilized to account for the macroscopic coverage probability and microscopic PLR. The expressions of devices’ coverage, steady state distribution, and PLR of services are derived for both scheduling schemes. Detailed system-level simulations are used to identify network design guidelines. The results show that the independent scheme provides better network scalability and consumes lower average transmit power at the devices, while the prioritized scheme enhances the PLR performance of high priority service and requires lower peak transmit power of devices.

Service-aware LSP selection with fuzzy based packet scheduling scheme for non-real time traffics

An essential solution is available in Multi-protocol label switching (MPLS), which solve the problems faced by present-day networks: speed, scalability, quality-of-service (QoS) management, and traffic engineering. This paper is an extension of work on Fuzzy based Packet Scheduling Algorithm (FPSA) combined with Packets Processing Algorithm (PPA) in an Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) networks. This will make provision for an intelligent service to the Label Switched Path (LSP) in MPLS networks. Several research work have been proposed on the MPLS Traffic Engineering. However, it is still imperative to further research on the effect of bandwidth increment on the core network using different mechanisms such as the analytical model of MPLS, expert-based packet scheduling algorithm for MPLS QoS support. Since MPLS is not able to provide intelligent routing, it is necessary to propose an intelligent expert system of FPSA combined with PPA. And analytical model of packet forwarding in the MPLS network would be given to provide suitable solution to traffic congestion and reliable services. Furthermore, the network model created using Network Simulator (NS 2), which carries non-real time application such as File Transfer Protocol (FTP) with bandwidth variations. The results obtained from trace files are interpreted by AWK script and used for the further analysis.

BBR-Based Congestion Control and Packet Scheduling for Bottleneck Fairness Considered Multipath TCP in Heterogeneous Wireless Networks

Aiming at improving the performance of Multipath TCP (MPTCP) in heterogeneous wireless network environments, in this paper, by taking the advantages Bottleneck Bandwidth and Round-trip propagation time (BBR) and considering bottleneck fairness, we propose BBR-based Congestion Control and Packet Scheduling scheme, called BCCPS. The proposed BCCPS first considers a BBR-based congestion control algorithm, MPTCP-BBR, for MPTCP by adaptively adjusting the sending rate of each subflow according to the real probing rate rather than the loss information to enhance the goodput while keeping bottleneck fairness with regular TCP. Then, considering that the different sending rates of the subflows in heterogeneous wireless networks will result in the problem of out-of-order (OFO) delivery, a fine-grained packet scheduling scheme is proposed to keep in-order delivery and so as to reduce the application layer completion time. The performance of the proposed scheme is evaluated via both NS-3 and real network scenarios. Experimental results show that our proposed scheme far outperforms existing MPTCP schemes in heterogeneous wireless environment.

Energy‐Efficient Distributed Packet Scheduling Optimization Strategy in Cooperative Vehicle Infrastructure Systems

In the cooperative vehicle infrastructure system (CVIS), due to the limitation of deployment conditions, some roadside units (RSUs) need to use renewable energy to supply power and transmit the fused sensor network’s data to the backbone network through the passing vehicles. Aiming at the problem of energy consumption and time delay guarantee of multiple self‐powered RSUs in the CVIS, a distributed packet scheduling optimization strategy for energy‐delay trade‐off in self‐powered RSUs is proposed. The strategy can minimize the system energy consumption by constraining the packet queue length of the self‐powered RSUs. A dynamic optimization model of distributed packet adaptive scheduling for multiple self‐powered RSUs is established based on the Lyapunov optimization theory. Based on the knapsack algorithm, the analytical algorithm of the optimization model is proposed. The simulation results show that the packet scheduling strategy can reduce the energy consumption and delay of the system by satisfying the upper limit of the packet queue length.

A Fair and Efficient Packet Scheduling Scheme for IEEE 802.16 Broadband Wireless Access Systems

A Fair and Efficient Packet Scheduling Scheme for IEEE 802.16 Broadband Wireless Access Systems

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

One of the major challenges facing the realization of cognitive radios (CRs) in future mobile and wireless communications is the issue of high energy consumption. Since future network infrastructure will host real-time services requiring immediate satisfaction, the issue of high energy consumption will hinder the full realization of CRs. This means that to offer the required quality of service (QoS) in an energy-efficient manner, resource management strategies need to allow for effective trade-offs between QoS provisioning and energy saving. To address this issue, this paper focuses on single base station (BS) management, where resource consumption efficiency is obtained by solving a dynamic resource allocation (RA) problem using bipartite matching. A deep learning (DL) predictive control scheme is used to predict the traffic load for better energy saving using a stacked auto-encoder (SAE). Considered here was a base station (BS) processor with both processor sharing (PS) and first-come-first-served (FCFS) sharing disciplines under quite general assumptions about the arrival and service processes. The workload arrivals are defined by a Markovian arrival process while the service is general. The possible impatience of customers is taken into account in terms of the required delays. In this way, the BS processor is treated as a hybrid switching system that chooses a better packet scheduling scheme between mean slowdown (MS) FCFS and MS PS. The simulation results presented in this paper indicate that the proposed predictive control scheme achieves better energy saving as the traffic load increases, and that the processing of workload using MS PS achieves substantially superior energy saving compared to MS FCFS.

Utilizing priority-based packet scheduling based on preemptive priority scheduling in wireless sensor networks

In this paper, each node has three levels of priority queues. Real-time packets are placed into the highest-priority queue and can preempt data packets in other queues. Non-real-time packets are placed into two other queues based on a certain threshold of their estimated processing time. Leaf nodes have two queues for real-time and non-real-time data packets since they do not receive data from other nodes and thus, reduce end-to-end delay. The priority packet scheduling scheme outperforms conventional schemes in terms of average data waiting time and end-to-end delay and also it reduces sensor energy consumption.

Shared Bottleneck-Based Congestion Control and Packet Scheduling for Multipath TCP

In order to be TCP-friendly, the original Multipath TCP (MPTCP) congestion control algorithm is always restricted to gain no better throughput than a traditional single-path TCP on the best path. However, it is unable to maximize the throughput over all available paths when they do not go through a shared bottleneck. Also, bottleneck fairness based solutions detect the bottleneck and conduct different congestion control algorithms at different bottleneck sets to increase throughput while remaining fair to single TCP. However, existing solutions generally detect shared bottlenecks through delay correlation and loss correlation between two flows, which often lead to misjudgement in dynamic and complex network scenarios. Therefore, in this paper, we first propose a new Shared Bottleneck based Congestion Control scheme, called SB-CC, which leverages ECN (Explicit Congestion Notification) mechanism to detect shared bottlenecks among subflows and estimate the congestion degree of each subflow. Then, with the congestion degree, SB-CC balances the loads among all subflows, and smooths out congestion window fluctuation. Also, in order to prevent throughput degradation due to out-of-order packets, we propose a Shared Bottleneck based Forward Prediction packet Scheduling scheme, called SB-FPS. SB-FPS distributes data according to the window size changes of each subflow, and thus could more accurately schedule data in shared bottleneck scenarios. We implement our proposed scheme in the Linux kernel and simulation platform to evaluate the performance in different scenarios. Measurement results indicate that our scheme can detect the bottleneck more accurately and improve the overall network performance while still keeping bottleneck fairness.

Transmission and Reception Alignment for User Equipment Energy Efficiency in LTE Networks

Energy efficiency has become an essential component in the design of communication protocols, especially for battery-enabled user equipment (UE) devices such as smartphones. In this letter, an energy-efficient UE packet scheduling scheme for LTE/LTE-Advanced cellular networks is proposed. It achieves energy savings by promoting the simultaneous occurrence of transmissions and receptions. Simulation results show that this approach improves the energy efficiency of the device by increasing the number of simultaneous transmissions and receptions; the network performance, however, suffers little impact.

Throughput enhanced scheduling (TES) scheme for ultra‐dense networks

SummaryThis paper proposes two novel packet scheduling schemes, called as throughput enhanced scheduling (TES) and TES plus (TES+), for future ultra‐dense networks. These schemes introduce two novel parameters to the scheduling decision making and reformulate the parameters used by the state‐of‐the‐art schemes. The aim is to have a more balanced weight distribution between delay and throughput‐related parameters at scheduling decisions. Also include a new telecommunications related parameter into scheduling decision making that has not been studied by popular schedulers. The performance of novel schemes is compared with well‐known schemes—proportional fairness (PF), exponential/proportional fairness (EXP/PF), and M‐LWDF. For performance evaluation, five performance metrics—average spectral efficiency and delay, quality of service (QoS) violation ratio, jitter, and Jain's fairness index—are investigated. The simulation results show that proposed schemes can outperform all the compared scheduling schemes.

Adaptive packet scheduling in IoT environment based on Q-learning

In the internet of things (IoT) environment consisting of various devices the traffic condition dynamically changes. Failure to process them in complying with the QoS requirement can significantly degrade the reliability and quality of the system. Therefore, the gateway collecting the data needs to quickly establish a new scheduling policy according to the changing traffic condition. The traditional packet scheduling schemes are not effective for IoT since the data transmission pattern is not identified in advance. Q-learning is a type of reinforcement learning that can establish a dynamic scheduling policy without any prior knowledge on the network condition. In this paper a novel Q-learning scheme is proposed which updates the Q-table and reward table based on the condition of the queues in the gateway. Computer simulation reveals that the proposed scheme significantly increases the number of packets satisfying the delay requirement while decreasing the processing time compared to the existing scheme based on Q-learning with stochastic learning automaton. And the processing time is also minimized by omitting unnecessary computation steps in selecting the action in the iterative Q-learning operations.