Fractional Exponent Coupling of RIO

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.

Loss differentiation is recommended as a service differentiation provided by an assured forwarding (AF) per-fop behavior (PHB) in differentiated service (DiffServ) architecture. An active queue management (AQM) technique is addressed as a suitable alternative to realize the service differentiation because the AF PHB should attempt to minimize long-term congestion while permitting short-term congestion in order to accommodate traffic bursts. In order to realize the AF PHB using an AQM scheme, it is desirable that the AQM scheme has DVO properties of sheltering and load tolerance in order to protect low drop precedence traffic and to prevent starvation of high drop precedence traffic. In this paper, we introduce another desirable property of assured drop probability. We modify an existing AQM algorithm for the property so that it assures a target drop probability in a properly provisioned network. Other properties of sheltering and load tolerance still hold for the modified AQM scheme. We evaluate it with other comparable schemes, i.e., WRED and RIO through simulation.

The differentiated services (DiffServ) architecture for provisioning quality of service (QoS) in the Internet provides a flexible framework for supporting a variety of services in heterogeneous environments. The assured forwarding (AF) per hop behaviour is a means of providing differential treatment of various DiffServ classes through achieving higher forwarding probabilities for higher priority classes. Extending the AF model to support fine grain QoS specification in W-CDMA environments allows mobile users in third-generation systems to benefit from the economical savings made possible by resource sharing among different classes of aggregated traffic. We consider extending the AF model to support the delivery of quasi constant bit-rate (QCBR) traffic streams on the downlink. In our study, each QCBR stream is assumed to have a prescribed average bit-rate and time duration, and is expected to be transmitted to completion without interruption at the requested bit-rate. The availability of such a service is beneficial for transmitting real-time traffic to mobile devices with limited power and buffering resources. The proposed mechanisms are based on integrating a suitable power-sharing structure for achieving differential treatment between classes with a crude power prediction algorithm for estimating the probability that no forced termination will occur at certain instants in the future. We compare the performance of a non-predictive scheme with a predictive scheme for different AF classes and source information bit-rates. Our results show that significant improvement in the forwarding probability, throughput, and power utilization for the DiffServ classes can be attained using the predictive scheme.

We present a virtual queue-based active queue management (AQM) scheme, called stabilized virtual buffer (SVB). The SVB scheme uses the packet arrival rate and queue length information to drop/mark packets probabilistically in a congested Internet router. System goodput, packet loss rate, average queue length, and stability of the queue are used to compare the proposed SVB scheme with prior AQM schemes (RED - random early detection; REM - random exponential marking; AVQ - adaptive virtual queue). Simulation results indicate that the SVB algorithm can provide better goodput and lower loss rate than the other three AQMs. The most striking feature of the proposed scheme is its robustness to workload fluctuations in maintaining a stable queue for different workload mixes (short and long flows) and parameter settings.

We study a Markovian single-server ticket queue where, upon arrival, each customer can draw a number from a take-a-number machine, while the number of the customer currently being served is displayed on a panel. The difference between the above two numbers is called the “virtual queue length.” We consider a nonhomogeneous population of customers comprised of two types: “regular” and “strategic.” Upon arrival, a regular customer, regardless of the value of the virtual queue length, draws a number from the machine, joins the queue and waits in the system until being served. A strategic customer, depending on the virtual queue length, may either join, leave, or go to “orbit” for a random duration. If, upon return from orbit, a strategic customer realizes that s/he missed her/his turn, s/he balks. Otherwise, s/he joins the queue and waits to be served. We analyze this intricate stochastic system, calculate its steady-state probabilities, derive the sojourn time’s Laplace–Stieltjes transform of a regular and of a strategic customer and calculate the system’s performance measures. Finally, an economic analysis is performed to determine the optimal mean orbiting time of strategic customers for two types of objective functions. Numerical examples are presented.

This document defines a general use Differentiated Services (DS) [Blake] Per-Hop-Behavior (PHB) Group called Assured Forwarding (AF). The AF PHB group provides delivery of IP packets in four independently forwarded AF classes. Within each AF class, an IP packet can be assigned one of three different levels of drop precedence. A DS node does not reorder IP packets of the same microflow if they belong to the same AF class.

As a scalable QoS (Quality of Service) architecture, DiffServ (Differentiated Service) mainly consists of two components: traffic conditioning at the edge of the DiffServ domain and simple packet forwarding inside the DiffServ domain. DiffServ has many advantages such as flexibility, scalability and simplicity. But when providing AF (Assured Forwarding) services, DiffServ has some problems such as unfairness among aggregated flows or among micro-flows belonging to an aggregated flow. In this paper, a feedback mechanism for AF aggregated flows is proposed to solve this problem. Simulation results show that this mechanism does improve the performance of DiffServ. First, it can improve the fairness among aggregated flows and make DiffServ more friendly toward TCP (Transmission Control Protocol) flows. Second, it can decrease the buffer requirements at the congested router and thus obtain lower delay and packet loss rate. Third, it also keeps almost the same link utility as in normal DiffServ. Finally, it is simple and easy to be implemented.

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.

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.

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.

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.

This paper analyzes a single server queueing system in which service is alternated between two queues and the server requires a (finite) switchover time to switch from one queue to the other. The distinction from classical results is that the sequence of switchover times from each of the queues need not be i.i.d. nor independent from each other; each sequence is merely required to form a stationary ergodic sequence. With the help of stochastic recursive equations explicit expressions are derived for a number of performance measures, most notably for the average delay of a customer and the average queue lengths under different service disciplines. With these expressions a comparison is made between the service disciplines and the influence of correlation is studied. Finally, through a number of examples it is shown that the correlation can significantly increase the mean delay and the average queue lengths indicating that the correlation between switchover times should not be ignored. This has important implications for communication systems in which a common communication channel is shared amongst various users and where the time between consecutive data transfers is correlated (for example in ad-hoc networks). In addition to this a number of notational mistakes in well-known existing literature are pointed out.

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.

In this paper, the performance of high speed uplink packet access (HSUPA) with mixed traffic (voice traffic and packet traffic) is studied by a simplified method. The principle of a simple method for mixed traffic study in HSUPA is presented. Then the system including E-TFC selection, node-B scheduling algorithm such as proportional-fair (PF), L1 HARQ and physical channels is modeled for this study. The performance of mixed traffic in HSUPA is studied by the way of qualitative and quantitative analysis with the system simulation results. The peak total cell packet throughput, up to 1.3 Mbps for pedestrian-A 3 kmph and 1.16 Mpbs for vehicle A 30 kmph, is reached when voice users are not available in the system. More voice users, which have the higher priority in the radio resource allocation than packet users, will lower the total cell packet throughput. The balance between voice users carried on DPCH and packet users carried on E-DCH is needed for the optimum performance. (4 pages)

We study the effects of different coupling strengths and network topologies on signal detection in small-world neuronal networks. Research has previously revealed that the ability of detecting subthreshold signals could be significantly enhanced by appropriately fine-tuning the noise intensity. Here we show that the coupling strength and the structure of the underlying network can also lead toward enhanced signal detection. In particular, we show that there are two levels of the coupling strength at which the subthreshold signal can be detected at an appropriate noise intensity and network structure. We also show that the network structure has little impact on signal detection if the coupling is weak. On the other hand, for intermediate coupling strengths, we show that the shorter the average path length, the better the signal detection. Finally, if the coupling is strong, we show that there exists an intermediate average path length at which signal detection becomes optimal.