A scheduling algorithm for packet delay differentiation

Input Queued (IQ) switch architecture has been of interest due to its low memory bandwidth requirement. A scheduling algorithm is required to schedule the transfer of packets through cross-bar switch fabric at every time slot. The performance, that is throughput and delay, of a switch depends on the scheduling algorithm. The maximum weight matching (MWM) algorithm is known to deliver 100% throughput under any admissible traffic. Leonardi et. al. (2001) obtained a nontrivial bound on the delay for the MWM algorithm under admissible Bernoulli i.i.d. traffic. There has been a lot of interesting work done over time to analyze throughput of scheduling algorithms. But apart from the work of Leonardi et al. there has not been any work done to obtain bounds on delay of scheduling algorithms. The MWM algorithm is perceived to be a very good scheduling algorithm in general and simulations have suggested that it performs better than most of the known algorithms in terms of delay. But it is very complex to implement. Hence many simple to implement approximations to MWM have been proposed. In this paper, we study a class of algorithms to MWM, which always obtain a schedule whose weight W differs from the weight of MWM schedule W* by at most f(W*), where f(.) is a sub-linear function. We call this difference in weight as approximation distance of algorithm from MWM. We denote this class of algorithms by 1-APRX. We prove that any 1-APRX algorithm is stable, that is, it delivers upto 100% of throughput under any admissible Bernoulli i.i.d. input traffic. Under any admissible Bernoulli i.i.d. traffic, we obtain bounds on the average queue length(equivalently delay) of the 1-APRX algorithms using a Lyapunov function technique, which was motivated in Leonardi et al. The delay bounds obtained for the 1-APRX algorithm are linearly related with the approximation distance, which matches the intuition that the better the weight of the schedule, the better the algorithm will perform. Interestingly, simulations show a linear relationship between the average queue length (equivalently delay) and the approximation distance. Thus, the approximation distance of a scheduling algorithm can serve as a metric to differentiate between the performance of different stable algorithms, even though throughput may be same for these algorithms. We also obtain a novel heuristic tighter bound on the average queue length (equivalently delay) under uniform Bernoulli i.i.d. traffic for MWM using a very simple argument.

Queue length is an important index of the efficiency of urban transport system. The traditional approaches seem insufficient for the estimation of the queue length when the traffic state fluctuates greatly. In this paper, the problem is solved by introducing the Cell Transmission Model, a macroscopic traffic flow, to describe the vehicles aggregation and discharging process at a signalized intersection. To apply the model to urban traffic appropriately, some of its rules were improved accordingly. Besides, we can estimate the density of each cell of the road in a short time interval. We, first, identify the cell, where the tail of the queue is located. Then, we calculate the exact location of the rear of the queue. The models are evaluated by comparing the estimated maximum queue length and average queue length with the results of simulation calibrated by field data and testing of queue tail trajectories. The results show that the proposed model can estimate the maximum and average queue length, as well as the real-time queue length with satisfactory accuracy.

The bifurcation and chaotic behavior of TCP may cause heavy oscillation of average queue length at routers and induce network instability. In this context, congestion control in the Internet is a challenging problem of key importance. This work presents a new discrete dynamical model of Random Early Detection (RED) using beta distribution for controlling bifurcations and chaos in the internet congestion control. The numerical analysis and the simulation experiments show that this new Active Queue Management (AQM) model can obtain the stable average queue length to the desired fix point. The model programmed with Python and Mathematica incorporates new parameters (α, β) that make it possible to stabilize oscillations of averaged router queue length and to be close to stationary state.

In this paper, we design a new virtual queue-based back-pressure scheduling algorithm (VBR) for achieving significant delay reduction in wireless sensor networks (WSN). Our algorithm design comes from an observation that classical back-pressure scheduling algorithm usually needs a long period of time to form a queue backlog-based gradient in a network, which decreases towards the sink in the network, before achieving stable packet delivery performance. To address this issue, VBR is designed to pre-build proper virtual queue-based gradient at nodes in a WSN, which is chosen to be a function of traffic arrival rate, link rate, and distance to sink, in order to be adaptive to different network and application environments while achieving high network performance. Moreover, the queue backlog differential between each pair of neighbor nodes is decided by their actual queue lengths and also their virtual queue lengths (gradient values). We prove that VBR can maintain back-pressure scheduling’s throughput optimality. Simulation result shows that VBR can obtain significant performance improvement in terms of packet delivery ratio, average end-to-end delay, and average queue length as compared with existing work.

Input-queued scheduling algorithms are designed to improve the throughput which use the input and output terminal in the router to obtain the maximum packets. And the length of the input queue is an important target, that can decides the superiority-inferiority about the capability of the input algorithm. With the existing algorithms, the main content about the scheduling algorithm are ensuring the packets’ high priority and low level. But most of these algorithms do not consider the length of queue. In this paper, we consider the change of queue length when the packets enter the input channel at the router, and put forward a shortest queue time-slot adapting algorithm (SQTA algorithm). First of all, SQTA algorithm can base the changes of transmittion time to control the packets’ sending time interval, and this action can make SQTA algorithm match the processing power about the router, and at the same time, this algorithm can reduce the length of the packets queue. Second, SQTA algorithm can control of a phenomenon that the packets may overflow when the length of queue is full, and that also make the packets transmitted more stable in network. The experimental result shows that this scheduling algorithm can control the length of queue effectively and reduce the waiting time with packets queue, It can be shorten the remaining service time, and can improve the network operation obviously.

The growing number of vehicles in Jordan has contributed to traffic congestion, particularly at roundabouts. Roundabouts deflect high volumes of traffic flow. To improve the performance of roundabouts, it is necessary to consider the impact of all components on traffic conditions, especially delay, queue length, and level of service (LOS), to reduce congestion and enhance efficiency and sustainability, etc. This study aims to (a) identify the optimal queue detector locations on all approaches at two selected roundabouts in Amman, Jordan, using micro-simulation (VISSIM) supported by programming (Python) software, and (b) validate the simulated models with the best LOS. Traffic and geometric data of roundabouts (Prince Faisal Bin al-Hussein, fifth; and Prince Rashid Bin Hassan, sixth roundabouts) were used for simulation purposes. The queue detector (across 15 distinct scenarios at various distances) and standard (base scenario, 50 m from the stop line) locations were assessed for optimal placement. The model validation was made based on all scenarios including signalized and non-signalized roundabouts. The best-case scenario for queue detector location was determined based on the highway capacity manual (HCM) criteria for measurement of effectiveness (MOE) at roundabouts. The optimal location was measured based on the duration of traffic delay (seconds), average queue length (m), and LOS. The optimal queue detector’s location was observed to be 97 m from the roundabout stop line. It can reduce the traffic delay (or speed up the traffic flow) by 85.25%. The average queue length can be reduced up to 76.76%. The LOS F status on the selected roundabouts can be improved to LOS D. Overall, the application of adaptive signal and queue detectors in appropriate locations at all roundabout approaches is crucial to improve imbalanced traffic flow while reducing delays.

Bus Rapid Transit (BRT) has emerged as a solution to traffic problems, especially in developing countries where cost is the major criteria to opt for a transit system. However, provision of BRT in existing right-of-way reduces the capacity available for other traffic. The problem gets severe at intersections. This paper presents the effect if the busway ends at an appropriate before the stop line at busy signalized at-grade intersections, and this space of bus lanes is made available to all traffic (heterogeneous traffic) at the intersection. VISSIM, a simulation tool, has been used to model the heterogeneous traffic and public transit lines under constraints of roadway geometry, vehicle characteristics, Indian driving behavior, and traffic controls. The effect is investigated with different random seeds to obtain reasonable results for analysis. The performance evaluation is done in terms of vehicle throughput; average queue length; maximum queue length; average delay time per vehicle; average speed; emission of carbon monoxide (CO), mono-nitrogen oxides (NOx), and volatile organic compounds (VOC); fuel consumption; and energy consumption. It has been observed that availability of bus lanes to other traffic for a reasonable distance before the intersection considerably reduces the average queue length, maximum queue length, average delay time per vehicle, and emission per vehicle, while there is an increase in vehicle throughput and average speed of all the vehicles. Thus it results in reduction of congestion and performance enhancement of at-grade intersections and networks.

Disk idle behavior has a significant impact on the energy efficiency of disk storage systems. For example, accurately predicting or extending the idle length experienced by disks can generate more potential opportunities to save energy. This paper employs a trace driven simulation to evaluate the impacts of different disk schedulers and queue length thresholds on the disk idle behavior. Experimental results give three implications: (1) Position based schedulers and long queue length thresholds can significantly reduce the maximal queue length and the average queue length. (2) Position based schedulers and long queue length thresholds can generate more idle periods which are shorter than 1 s, but they do not affect those long idle periods contained in the modern server workloads. (3) Disk idle periods demonstrate both self-similarity and weak long-range dependence, and the disk schedulers and queue length thresholds do impact the Hurst parameter and the correlation behavior of the workloads. The analysis results in this paper provide useful insights for designing and implementing energy efficient policies for the disk drive based storage systems.

Active Queue Management (AQM) is an active research area in the Internet community. Random Early Detection (RED) is a typical AQM algorithm, but it is known that it is difficult to configure its parameters and its average queue length is closely related to the load level. This paper proposes an effective fuzzy congestion control algorithm based on fuzzy logic which uses the predominance of fuzzy logic to deal with uncertain events. The main advantage of this new congestion control algorithm is that it discards the packet dropping mechanism of RED, and calculates packet loss according to a preconfigured fuzzy logic by using the queue length and the buffer usage ratio. Theoretical analysis and Network Simulator (NS) simulation results show that the proposed algorithm achieves more throughput and more stable queue length than traditional schemes. It really improves a router’s ability in network congestion control in IP network.

In the design of traffic signals it is often desirable to be able to estimate the queue length at the beginning of the green period. The queue length at this period of the cycle will normally be the greatest experienced because during the green period the queue is being discharged at a rate which for practical purposes must be greater than the flow on the approach.

The HSBQ algorithm is the one of active queue management algorithms, which orders to avoid high packet loss rates and control stable stream queue. That is the problem of calculation of the drop probability for both queue length stability and bandwidth fairness. This paper proposes the HSBQ, which drop the packets before the queues overflow at the gateways, so that the end nodes can respond to the congestion before queue overflow. This algorithm uses the change of the average queue length to adjust the amount by which the mark (or drop) probability is changed. Moreover it adjusts the queue weight, which is used to estimate the average queue length, based on the rate. The results show that HSBQ algorithm could maintain control stable stream queue better than group of congestion metric without flow information algorithm as the rate of hybrid satellite network changing dramatically, as well as the presented empiric evidences demonstrate that the use of HSBQ algorithm offers a better quality of service than the traditionally queue control mechanisms used in hybrid satellite network.

This paper proposes a router congestion control mechanism called PRED (Prediction-enabled RED), a more adaptive and proactive version of RED (Random Early Detection). In essence, PRED predicts its queue length for an early detection of possible congestion alerts in the near future and operates adaptively to the predicted changes in traffic patterns. Typically, PRED does this by first making prediction about average queue length and then using the predicted average queue length to adjust three classic RED parameters max th, min th, and max p. The incoming packets after the adjustment are now being dropped with the new probability defined by updated parameters. Due to its adaptability and proactive reaction to network traffic changes, PRED can be considered as a novel solution to dynamically configure RED. Extensive simulation results from NS-2 simulator are presented to verify the performance and characteristics of PRED.

AQM (Active Queue Management) is an effective scheme to enhance congestion control. Most of the mobile networks today use queue management to manage and improve network performance. In this paper, we deploy AQM mechanism in Mobile Ad Hoc networks to manage the packets queue in the network nodes. By modeling a node with multi-receiver in Ad Hoc networks, we develop an analytical model for a multi-server and finite capacity queuing system with AQM mechanisms under multi-classes of traffic. We consider multiple classes of traffic modeled by Poisson arrival and exponentially service processes. It deduced the performance metrics including the average queue length, system throughput, probability of drop packet and the average waiting time. Then, it illustrated the relationship between above metrics and the number of servers. Finally, by demonstrating the effective number of servers, it illuminate that we should deploy appropriate number of servers for a certain condition.

Reinforcement learning is one of the most widely used methods for traffic signal control, but the method experiences issues with state information explosion, inadequate adaptability to special scenarios, and low security. Therefore, this paper proposes a traffic signal control method based on the efficient channel attention mechanism (ECA-NET), long short-term memory (LSTM), and double Dueling deep Q-network (D3QN), which is EL_D3QN. Firstly, the ECA-NET and LSTM module are included in order to lessen the state space’s design complexity, improve the model’s robustness, and adapt to various emergent scenarios. As a result, the cumulative reward is improved by 27.9%, and the average queue length, average waiting time, and CO2 emissions are decreased by 15.8%, 22.6%, and 4.1%, respectively. Next, the dynamic phase interval tgap is employed to enable the model to handle more traffic conditions. Its cumulative reward is increased by 34.2%, and the average queue length, average waiting time, and CO2 emissions are reduced by 19.8%, 30.1%, and 5.6%. Finally, experiments are carried out using various vehicle circumstances and unique scenarios. In a complex environment, EL_D3QN reduces the average queue length, average waiting time, and CO2 emissions by at least 13.2%, 20.2%, and 3.2% compared to the four existing methods. EL_D3QN also exhibits good generalization and control performance when exposed to the traffic scenarios of unequal stability and equal stability. Furthermore, even when dealing with unique events like a traffic surge, EL_D3QN maintains significant robustness.

Optimizing acyclic traffic signal switching sequences through an Extended Linear Complementarity Problem formulation