Packet Scheduling Research Articles

Wireless scheduling with deadline and power constraints

This paper studies the scheduling problem in a co-located wireless network under both the deadline and power constraints. We consider a frame-based time-slotted system. The channel condition of a link remains constant within each frame but varies from frame to frame. Packets with hard deadlines arrive at the transmitters at the beginning of each frame, and will be discarded if missing their deadlines, which are in the same frame. Each of the links is associated with a quality of service (QoS) constraint and an average transmit power constraint. A MaxWeight-type problem is formulated for achieving throughput optimality. The computational complexity of solving the MaxWeight-type problem using the exhaustive search is exponential even for a single-link system. To overcome this difficulty, we propose a greedy algorithm, named PDMax (Power and Deadline constrained MaxWeight), with complexity O(nlog(n)). PDMax schedules packets according to their deadlines and incremental weight gains to the objective of the MaxWeight-type problem. We prove that PDMax is throughput optimal. Our simulations further show that PDMax outperforms both the Largest-Debt-First and the greedy-MaxWeight algorithms in terms of average packet delivery ratio and average transmit power.

Minimum Length Minimum Delay Scheduling Using Artificial Butterfly Optimization (ABO) Algorithm in Manet

Recently, the requirement for various real-time applications over Mobile ad-hoc networks (MANETs) has been increasing tremandously. Packet scheduling and routing algorithms are the important factors for improving the Quality of service (QoS) parameters over the MANETs. Still, it is not easy to proficiently aid multimedia broadcast with its stringent delay constraints over MANETs. Moreover, there are several problems in assisting QoS over MANETs owing to interference amid nodes in the network. In this paper, Minimum Length Minimum Delay Scheduling (MLMDS) using Artificial Butterfly Optimization (ABO) algorithm is proposed. A fitness function for ABO is derived in terms of the round trip scheduling delay, the conflict graph and the amount of slots in the data sub-frame. A slot selection algorithm is proposed such that the selected slot is supposed to yield lesser delay and spatial reuse. By simulation results, it has been shown that MLMDS achieves minimum delay with higher throughput.

An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks

Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service (QoS) in the 5th Generation (5G)-beyond and the 6th Generation (6G) networks. However, effective slicing of Radio Access Network (RAN) is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks. In this paper, we propose a self-sustained RAN slicing framework, which integrates the self-management of network resources with multiple granularities, the self-optimization of slicing control performance, and self-learning together to achieve an adaptive control strategy under unforeseen network conditions. The proposed RAN slicing framework is hierarchically structured, which decomposes the RAN slicing control into three levels, i.e., network-level slicing, next generation NodeB (gNodeB)-level slicing, and packet scheduling level slicing. At the network level, network resources are assigned to each gNodeB at a large timescale with coarse resource granularity. At the gNodeB-level, each gNodeB adjusts the configuration of each slice in the cell at the large timescale. At the packet scheduling level, each gNodeB allocates radio resource allocation among users in each network slice at a small timescale. Furthermore, we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing control. With the proposed RAN slicing framework, the QoS performance of emerging services is expected to be dramatically enhanced.

Linear Network Coding for Dynamic Rate Adaptation with Global Resource Tradeoff in Distributed 5G LAA Network

5G cellular network will drive towards higher energy efficiency, lower latency and higher reliable wireless networks. The key contributions can summarize as follows: (1) this paper proposes a feasible method to compute the expected rate and approach the optimal capacity region by upper and lower bounds in 5G cellular network, (2) develops linear network coding and dynamic rate adaptation to improve the throughput and reliability of LAA network, (3) proposes a new E-PDP-MNLC (Efficient Protocol Priority-based Distributed Paired Multicast Network Linear Coding) scheme for LAA network coding. • Networks → Network layer protocols → Routing protocols • Networks → Network algorithms → Data path algorithms → Packet scheduling → Control path algorithms → Network control algorithms.

Remote state estimation with usage-dependent Markovian packet losses

In this paper, we consider a problem of packet scheduling in the setting of remote estimation with usage-dependent Markovian packet losses. A sensor measures the state of a discrete-time linear process, computes the estimate via a local Kalman filter, and sends the packets to a remote estimator via a network. The link state evolves as a two-state Markov chain, and its state transition depends on the network usage. The aim is to design the scheduling policy which balances the estimation quality and the energy consumption. We identify the problem as a Markov decision process (MDP) and prove the structural properties of the optimal policy. Furthermore, based on the structural properties, we derive the sufficient and necessary condition of the mean square stability of the remote estimator. Simulation examples are provided to illustrate the results.

Fair-Hierarchical Scheduling for Diversified Services in Space, Air and Ground for 6G-Dense Internet of Things

The 6G space-air-ground integrated network can achieve global full-area three-dimensional coverage and ultra-wide-area broadband access capabilities anytime and anywhere. Through the integration of satellite networks and terrestrial networks, it can provide a better user experience and has become the core development direction of 6G networks. Inspired by the potential of service differentiation ability of 6G IEEE 802.11ax protocols (Wi-Fi 6), a Data-driven Fair-Hierarchical Scheduling (DFHS) is proposed in this paper to schedule packets from diversified applications in dense Internet of Things (IoT) networks, which allows data streams to fairly share the spectrum and at the same time satisfy delay requirements. Our DFHS is divided into the outer-layer and inner-layer schedulers. First, diversified packets of IoT services are classified into categories according to delay requirement and transmission frequency. Then the outer-layer scheduler assigns classified packets to different Access Categories (AC) with differentiated channel competition capabilities. Next, the inner-layer scheduler optimally schedules the AC queues according to the packet access response ratio. Particle Swarm Optimization algorithm is further introduced to solve the optimal packet scheduling problem. Numerical results demonstrate that the DFHS outperforms the classical FCFS, the latest EARS and Tian's schemes in terms of service missing ratio, queueing delay and fair index.

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

A Packet Scheduling Method Based on Dynamic Adjustment of Service Priority for Electric Power Wireless Communication Network

With the development of the energy Internet, power communication services are heterogeneous, and different power communication services have different business priorities. The power communication services with different priorities have different requirements for network bandwidth and real-time performance. For traditional unified service, a scheduling method cannot meet these service requirements at the same time, and electric power communication network cannot guarantee the quality of service. Therefore, how to make full use of the time-varying characteristics of communication resources to meet the business needs of different priorities and achieve the goals of high resource utilization and transmission quality has become one of the urgent problems in the power communication network. For this reason, in order to adapt to the real-time congestion of the network, we have designed a packet scheduling method based on the dynamic adjustment of service priority, which dynamically adjusts the priority of the power service on the node; in addition, an evaluation method for the trust value of wireless forwarding nodes is introduced to improve the security of data transmission; and finally, we valuate the channel quality to establish a reasonable and efficient packet scheduling mechanism for services of different priorities. Simulation results show that this method improves the communication performance of high-priority services and improves the spectrum resource utilization of the entire system.

A Comparative Simulation Study of Deterministic and Stochastic Strategies for Reduction of Packet Reordering in Multipath Data Streaming

Multipath routing gains clear network performance advantages for data streaming in networked systems with high path diversity. The level of packet reordering, however, becomes higher: distant packets are reordered, the application performance is reduced due to head-of-line blocking at the destination, and a large resequencing buffer is needed for sorting incoming packets. In this paper, we study by simulation the stochastic compensation effect to reduce packet reordering. If a source randomizes packet scheduling into multiple paths of random transmission delays, then these two sides of randomness “quench” each other. We perform comparison experiments to test this hypothesis in various multipath configurations, focusing on deterministic vs. randomized strategies of packet scheduling. The experiments confirm the existence of the stochastic compensation effect and its considerable influence on the application performance. © 2016, UK Simulation Society. All rights reserved.

Latency Constrained Non-Orthogonal Packets Scheduling With Finite Blocklength Codes

In this paper, we investigate the offline packets scheduling of a downlink system with two heterogeneous latency-constrained users. Specifically, user 1 is a latency-tolerant user which has one packet with long latency requirement, while user 2 is latency-sensitive user which has multiple packets with short latency requirements. To precisely characterize the transmission latency, the finite blocklength codes (FBC) capacity formula is used to measure the achievable date rates of the users. To cope with the heterogeneous latency requirements of users, a hybrid NOMA scheme is proposed. In the hybrid NOMA scheme, the packets of user 2 are non-orthogonally delivered with the packet of user 1 in a sequential way by taking the packets arrivals and deadlines into consideration. Due to complicate FBC capacity formula and the coupling effects of the variables, the formulated transmission energy minimization problem is challenging to solve. However, by properly utilizing the the fact that the transmission energy is monotonically decreasing with the blocklength, the optimal transmission blocklength and power of user 1 are derived in semiclosed-forms; while the remaining problem, for determining the transmission blocklength and power of user 2, is efficiently solved by using a successive upperbound minimization based algorithm. The simulation results demonstrate the efficacy of the proposed hybrid NOMA scheme.

Deadline Constrained Packet Scheduling in the Presence of an Energy Harvesting Jammer

Maintaining data communication in the presence of jammer nodes is an important feature required in several modern wireless networks. In this article, we analyze a three node system, where a legitimate link between the transmitter and the receiver is adversarially affected by independent Gaussian noise injected by a jammer. The transmitter is battery powered, and has data arriving as packets. All the data need to be delivered to the receiver before a common deadline. The transmitter's goal is to accomplish this task using as minimum an energy as possible, while the jammer's objective is the opposite, viz. increase the transmitter's energy as much as possible by strategically injecting noise. We consider a jammer node which relies on dynamic energy harvesting sources to power its hardware. The adversarial interactions are modeled by posing a min-max game between the transmitter and the jammer. Solving this, we derive the equilibrium scheduling policies of the transmitter and the jammer for the offline model, and present an iterative algorithm to compute these policies. Competitive online scheduling policies are then proposed, whose competitive ratio is bounded using ideas from the offline policies derived earlier. A constant competitive ratio is shown when the last data packet does not arrive too early, and a significant fraction of the jamming energy is harvested before this arrival.

SharpEdge: an asynchronous and core-agnostic solution to guarantee bounded-delays

What are the key properties that a network should have to provide bounded-delay guarantees for the packets? In this paper, we attempt to answer this question. To that end, we explore the theory of bounded-delay networks and provide the necessary and the sufficient conditions required to have deterministic bounded-delays in the network. We prove that as long as a network is work-conserving, independent of the packet scheduling and queue management algorithms used in the switches, it is sufficient to shape the traffic~\textit{properly} at the edge of the network to meet hard bounded-delays in the network. Using the derived theorems, we present SharpEdge, a novel design to meet deterministic bounded-delays in the network. To the best of our knowledge, SharpEdge is the first scheme that can meet all following key properties: 1) it supports coexistence of different classes of traffic, while it can guarantee their different required bounded-delays 2) it does not require any changes in the core of the network, 3) it supports both periodic and bursty traffic patterns, and 4) it does not require any time synchronization between network devices.

An Adaptive and Efficient Packet Scheduler for Multipath TCP

A multipath TCP (MPTCP) scheduler selects a suitable path for each data packet and helps to improve the performance of MPTCP. In this paper, we analyze various existing schedulers and find some current open issues like head-of-line blocking problem, receive window limitation, and out-of-order packet delivery. All these issues degrade the performance of MPTCP, which can be overcome by the careful design of an MPTCP scheduler. We then propose a new MPTCP packet scheduler named as an adaptive and efficient packet scheduler (AEPS) that not only overcomes these issues but also provides high throughput with a minimum completion time by utilizing the bandwidth of all the available paths. AEPS delivers the data packets in-order to the receiver, and its performance does not affect by the size of the receiver buffer and data size that is being transferred. We show experimentally and analytically that the proposed scheduler outperforms the existing schedulers.

Machine learning based lightweight interference mitigation scheme for wireless sensor network

The interference issue is most vibrant on low-powered networks like wireless sensor network (WSN). In some cases, the heavy interference on WSN from different technologies and devices result in life threatening situations. In this paper, a machine learning (ML) based lightweight interference mitigation scheme for WSN is proposed. The scheme detects and identifies heterogeneous interference like Wifi, bluetooth and microwave oven using a lightweight feature extraction method and ML lightweight decision tree. It also provides WSN an adaptive interference mitigation solution by helping to choose packet scheduling, Acknowledgement (ACK)-retransmission or channel switching as the best countermeasure. The scheme is simulated with test data to evaluate the accuracy performance and the memory consumption. Evaluation of the proposed scheme’s memory profile shows a 14% memory saving compared to a fast fourier transform (FFT) based periodicity estimation technique and 3% less memory compared to logistic regression-based ML model, hence proving the scheme is lightweight. The validation test shows the scheme has a high accuracy at 95.24%. It shows a precision of 100% in detecting WiFi and microwave oven interference while a 90% precision in detecting bluetooth interference.

Understanding Cooperative Data Exchange Problem in Multi-Hop Wireless Network

This letter theoretically formulates the coded cooperative data exchange (CCDE) problem in multi-channel multi-hop wireless networks, where a set of nodes initially hold a subset of packets and wish to retrieve all desired packets via multi-hop wireless communication. At first, we propose the model of tentative transmission, with which we can jointly consider resource allocation, transmission scheduling, and transmitted packet scheduling. Then, based on the time expanded graph, we convert the CCDE problem in multiple time slots into a single source and multiple sinks network (SSMN) within a time slot. Finally, the relationship between the required time slots and the number of channels is theoretically formulated based on the constructed SSMN. Experiments demonstrate that the gain due to the increasing number of channels flattens.

A Study on Packet Scheduling Algorithms for Healthcare Contents over Fifth Generation (5G) Mobile Cellular Network

A Study on Packet Scheduling Algorithms for Healthcare Contents over Fifth Generation (5G) Mobile Cellular Network

Improving network performance with an integrated priority queue and weighted fair queue scheduling

<p>Quality of service (QoS) is the measure of network service availability and transmission. There are many factors influencing QoS among which one is the increasing number of network service users. The increase in the number of network service users and communication traffic causes network congestion. And the traffic congestion results in delay or packet loss and jitter variation. As a result, an organization’s network quality deteriorates and or even becomes unavailable. Therefore, to deliver a high quality network service to the users, a solution that avoids network traffic congestion is needed. In this study, the causes for network traffic congestion and the best solutions to eliminate traffic congestion in a network with congestion management and avoidance using an integrated priority queue (PQ) and weighted fair queue (WFQ) packet scheduling algorithms is proposed.</p>

Modified Proportional Fair Scheduling Algorithm for Heterogeneous LTE-A Networks

Growing of time-sensitive applications such as streaming multimedia, voice over IP and online gaming required strongly support from mobile communication technology. So, the persistent need for wireless broadband technologies such as LTE-A is essential. LTE-A can achieve QoS in an efficient manner by using a reliable packet scheduling algorithm. It also supports good cell coverage by using heterogeneous capability. In this paper, modifications of proportional fair (PF) algorithm are proposed with different methods to compute the average throughput, which is the main and important parameter in the PF cost function. These methods are geometric mean, root mean square and arithmetic mean. Vienna simulator is used to study the performance of the proposed algorithms. A comparison between PF modifications and the most famous algorithms (the original PF and Best CQI algorithms) with various UE velocities is introduced. Average UE throughput, spectral efficiency, energy per bit, cell throughput and fairness are used as performance indicators. The results expose that QPF has best improved values for spectral efficiency, energy per bit and fairness by 8.4%, 14%, and 9.3%, respectively than original PF. However, Best CQI has a better value of average UE and cell throughput than all algorithms of 2% and 1.8% in low and medium UE velocity, respectively, but the best value of all types of throughput at high speed is gained by QPF. QPF and GMPF has the same performance in fairness with all UEs speeds.

Energy efficient robust scheduling of periodic sensor packets for discrete rate based wireless networked control systems

Wireless networked control systems (WNCSs) require the design of a robust scheduling algorithm that meets the stringent timing and reliability requirements of control systems, despite the limited battery resources of sensor nodes and adverse properties of wireless communication for delay and packet errors. In this article, we propose a robust delay and energy constrained scheduling algorithm based on the exploitation of the mostly pre-known periodic data generation nature of sensor nodes in control systems. We first formulate the joint optimization of scheduling, power control and rate adaptation for discrete rate transmission model, in which only a finite set of transmission rates are supported, as a Mixed-Integer Non-linear Programming problem and prove its NP-hardness. Next, we propose an optimal polynomial-time power control and rate adaptation algorithm for minimizing the transmission time of a node subset. We then design a novel polynomial-time heuristic scheduling algorithm based on first determining the concurrently transmitting node subsets and then distributing them uniformly over time by a modified Karmarkar-Karp algorithm. We demonstrate the superior performance of the proposed scheduling algorithm in terms of robustness, delay and runtime on the Low-Rate Wireless Personal Area Network (LR-WPAN) simulation platform, which we developed in network simulator-3 (ns3).

Downlink Radio Resource Management Through CoMP and Carrier Aggregation for LTE-Advanced Network

Long Term Evolution-Advanced (LTE-A) offers several new technologies to improve the performance of the user. However, poor received signal and interference from adjacent cells in the cell-edge area can reduce the efficiency of using individual technology. Therefore, the cell-edge users have lower throughput compared to the other users in the cell and LTE-A standard. An efficient downlink radio resource management scheme is proposed in this paper by combining the coordinated multipoint transmission and reception technique along with carrier aggregation technique to achieve higher throughput for the cell-edge user and better overall performance. The proposed method jointly transmits multiple component carriers to the cell-edge user from different cells to increase the bandwidth, strengthen the received signal, and reduce the interference while it satisfies several constraints. Modified largest weighted delay first packet scheduling algorithm is deployed for resource allocation, which takes into account the delay parameters, the probability of packet loss, and data rates of the user. The obtained system-level simulation results show that the proposed method significantly enhances the throughput performances, spectral efficiency, and fairness index, compared with the existing conventional methods.