Queue Overflow Research Articles

Delay analysis and optimality of the renewal access protocol

For many years, the IEEE 802.11 distributed coordination function (DCF) has been widely used as a dominant medium access control (MAC) protocol in wireless networks and a large number of works have been done for analyzing and improving its performance. In our earlier work, as a substitute of the IEEE 802.11 DCF, a simple MAC protocol, called the renewal access protocol (RAP), is proposed. The RAP adopts all of the legacy 802.11 standard but the backoff stage feature. Each terminal selects its backoff counter value from a fixed sized window according to a priori given selection distribution in the RAP, regardless of the packet transmission result. It is shown that, if a Poisson distribution is used as the selection distribution, then the resulting RAP achieves high short-term fairness as well as optimal throughput. In this work, we analyze the relation between delay performance and the selection distribution of the RAP. With the help of effective bandwidth theory, we derive the conditions for the selection distribution of the RAP that optimizes the queue overflow probability. We also construct the delay optimal selection distribution satisfying the optimal conditions for throughput and delay. However, we show that the use of the delay optimal selection distribution results in an extremely slow convergence to steady state compared with that of the Poisson selection distribution. Moreover, we show that the Poisson selection distribution provides near-optimal delay performance. Therefore, we conclude that the use of a Poisson selection distribution is still recommended even from the delay perspective.

Buffer-aware adaptive resource allocation scheme in LTE transmission systems

Dynamic resource allocation scheme is a key component of 3GPP long-term evolution (LTE) for satisfying quality-of-service (QoS) requirement as well as improving the system throughput. In this paper, a buffer-aware adaptive resource allocation scheme for LTE downlink transmission is proposed for improving the overall system throughput while guaranteeing the statistic QoS and keeping certain fairness among users. Specifically, the priorities of the users’ data queues in the base station are ranked by their remaining life time or their queue overflow probability which is estimated by applying large deviation principle. An online measurement based algorithm which requires no statistical knowledge of the network conditions uses the queue priorities to dynamically allocate the resource blocks (RBs) for avoiding buffer overflow and providing statistic QoS guarantee. The simulation results show that the proposed algorithm improves the throughput and fairness while considerably reducing the average bit loss rate.

Wireless scheduling with partial channel state information: large deviations and optimality

We consider a server serving a time-slotted queued system of multiple packet-based flows, where not more than one flow can be serviced in a single time slot. The flows have exogenous packet arrivals and time-varying service rates. At each time, the server can observe instantaneous service rates for only a subset of flows (selected from a fixed collection of observable subsets) before scheduling a flow in the subset for service. We are interested in queue length aware scheduling to keep the queues short. The limited availability of instantaneous service rate information requires the scheduler to make a careful choice of which subset of service rates to sample. We develop scheduling algorithms that use only partial service rate information from subsets of channels, and that minimize the likelihood of queue overflow in the system. Specifically, we present a new joint subset-sampling and scheduling algorithm called Max-Exp that uses only the current queue lengths to pick a subset of flows, and subsequently schedules a flow using the Exponential rule. When the collection of observable subsets is disjoint, we show that Max-Exp achieves the best exponential decay rate, among all scheduling algorithms that base their decision on the current (or any finite past history of) system state, of the tail of the longest queue. To accomplish this, we employ novel analytical techniques for studying the performance of scheduling algorithms using partial state, which may be of independent interest. These include new sample-path large deviations results for processes obtained by non-random, predictable sampling of sequences of independent and identically distributed random variables. A consequence of these results is that scheduling with partial state information yields a rate function significantly different from scheduling with full channel information. In the special case when the observable subsets are singleton flows, i.e., when there is effectively no a priori channel state information, Max-Exp reduces to simply serving the flow with the longest queue; thus, our results show that to always serve the longest queue in the absence of any channel state information is large deviations optimal.

Improving VANET Performance with Heuristic and Adaptive Fuzzy Logic Scheme

Rapid topology change in vehicular ad hoc network (VANET) is common due to the inherent high mobility nodes and unpredictable environments in VANET. In order to ensure efficient packet transmission, nodes in VANET should react adaptively to topology change in VANET. In this paper, we present a new heuristic and adaptive fuzzy logic scheme (HaFL), which adapts the contention window size and transmission power according to network and traffic conditions. The current existing schemes in VANET utilize only a single parameter to optimize the contention window and transmission power without consideration on the effects of interference as one of the main factors in VANET transmission degradation. In VANET, packet loss can occur at different stages of transmission due to interference or due to elapsed time. In the proposed HaFL, fuzzy logic is used to adaptively optimize the contention window size based on three parameters namely collision rate, SINR and queue overflow which represent packet drop at different stages of transmission. Transmission power which is usually a static parameter is also optimized with consideration on the effects of VANET interference in the proposed HaFL. The performance of the proposed HaFL is evaluated in Vehicles in Network Simulation with road traffic simulator, Simulation of Urban mobility. Simulation results show that the proposed HaFL demonstrates adaptability with improved throughput, low end-to-end delay and higher packet success rate in comparison with the default IEEE802.11p and existing schemes.

MTADF : Multi Hop Traffic Aware Data for Warding for Congestion Control in Wireless Sensor Networks

In the past few years there is a remarkable change in the field of wireless sensor networks. Congestion occurs when there is a heavy traffic in the network. The heavy traffic in the network leads to wastage of energy and packet loss. Traffic Aware Dynamic Routing algorithm mitigates congestion by using one hop neighbor routing, hence throughput of the network is low. This paper proposed a Multi hop based Data Forwarding Technique to mitigate congestion. Queue length field and depth potential field play a major role to divert the traffic in the network to the alternate paths. The high traffic load leads to data queue overflow in the sensor nodes, these results in loss of important information about important events. Multi hop Traffic-Aware Dynamic Routing algorithm addresses congestion using depth potential field and queue length potential field. The algorithm forwards data packets around the congestion areas and scatters the excessive along multiple paths. The nodes with less load are efficiently utilized in response to congestion. The main aspect of the algorithm is to construct two independent potential fields using depth and queue length. Queue length field solves the traffic-aware problem. Depth field creates a backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential field to make dynamic decision for data forwarding. Simulations are conducted to evaluate the performance of our proposed algorithm and our proposed scheme performs better compared to previous work.

A neural network based congestion control algorithm for content-centric networks

Communication across the Internet has transformed over the years, generated primarily by changes in the importance of content distribution. In the twenty-first century, people are more concerned with the content rather than the location of the information. Content-Centric Networking (CCN) is a new Internet architecture, which aims to access content by a name rather than the IP address of a host. Having the content, CCN which is natively pull-based functions based on the requests received from customers. It is also combined with the availability of in-network chaching. Because of the availability of in-network caching in CCN, chunks may be served by multiple sources. This multi-path transfer in CCN makes TCP-based congestion control mechanisms inefficient for CCN. In this paper a new congestion control algorithm is proposed, which is based on Neural Network prediction over content-centric networks. The designed NN is implemented in each router to predict adaptively the existence of the congestion on link given the current status of the network. The results demonstrate that the proposed congestion control algorithm can effectively improve throughput by 85.53%. This improvement is done by preventing queue overflow from happening, which will result in reductions in packet drop in the network. Keywords: Content-Centric Network, Congestion Control, Drop Prediction, Named Data Networking, Neural Network.

Capacity Model for Signalized Intersection under the Impact of Upstream Short Lane

In Highway Capacity Manual (HCM) 2010, various factors were considered to adjust the base saturation flow rate for capacity analysis of signalized intersections. However, impacts of short lane were only addressed by the adjustment for lane utilization based on deterministic analysis. Such analysis does not consider potential queue overflow and blockage resulted from short lane. This paper presented a theoretical model for estimating lane group capacity at signalized intersections with consideration of upstream short lane effects based on probability theory. Three scenarios of signal phase sequences were taken into account, reflecting the relationship between movement that may cause blockage and movement that may be blocked. The proposed model was validated by VISSIM simulation. Results of extensive analysis showed that the upstream short lane may have a substantial impact on capacity: the capacity is positively correlated with effective green time length and negatively correlated with short lane length. Among the three scenarios of phase sequence plans, the worst one causing the largest decrease in lane group capacity is phase sequence case 2 (the movement that may cause blockage follows the movement that may be blocked). It is shown that there is an optimal cycle length to obtain maximum capacity when upstream short lane exists.

A Research of Network Congestion Control Based on Fuzzy PID Arithmetic

This paper first introduces definition and cause for congestion control as well as TCP/IP congestion control mechanism, focusing on introducing active queue management arithmetic in IP congestion control mechanism, and points out its important function in congestion control. The method of active queue management arithmetic which solves full queue of routers is dropping arrived data packets before the queue is full so that end-note is able to respond before the queue overflows, avoiding the actual occurrence of congestion. RED arithmetic is a famous AQM arithmetic and congestion control based on control theory has become a new research focus.

Pipelined Statistical Cipher Feedback: A New Mode for High-Speed Self-Synchronizing Stream Encryption

In this paper, we introduce a new block cipher mode of operation targeted to providing high-speed hardware-based self-synchronizing stream encryption. The proposed mode is a modification of statistical cipher feedback (SCFB) mode and is designed to be implemented using pipeline architectures for the block cipher. We refer to the mode as pipelined SCFB mode or PSCFB. In this paper, we consider the implementation characteristics and show that PSCFB is able to achieve speeds that are very close to pipelined block cipher implementations configured for counter mode. Such speeds are achieved with modest latency through the system and a small amount of memory required for the system queues with a provable guarantee of no queue overflow. Further, we examine the characteristics of PSCFB mode in response to bit errors and synchronization losses in the communication channel. Specifically, we show that the error propagation factor is modest and comparable to conventional SCFB and that synchronization recovery delay is reasonable given the expectation that synchronization loss is infrequent. Given the high efficiency and good communication characteristics of the mode, it is concluded that PSCFB is an excellent choice for high-speed network applications requiring stream-oriented encryption with self-synchronizing capabilities.

A Kind of Improved RED Algorithm of WSN Oriented to Self-Similar Traffic

Active queue management algorithm works at network routing node. It notifies terminal system control transmission rate in advance with discard or mark packet before buffer queue overflows, so as to avoid congestion. Aiming at characteristics of wireless sensor network (WSN) and based on introduction of traditional RED algorithm, the paper introduced H parameter to represent traffic similarity to original linear relationship between packet loss probability and average queue length to form the non-linear relationship related to self-similarity. Then a kind of improved RED algorithm of WSN oriented to self-similar traffic was proposed. The detail implementation flow was also provided. Simulation based on OPNET shows that the improved RED is superior to traditional RED algorithm in steady performance.

Traffic-aware routing protocol for wireless sensor networks

In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.

On the Elasticity of Marking Functions in an Integrated Network

Much of the research on network modeling and analysis has focused on the design of end controllers and network algorithms with the objective of stability and convergence of the transmission rate. However, a network is typically composed of a mixture of both controlled elastic flows and uncontrolled real-time flows. In this paper, we study the effects of marking elasticity (which characterizes how aggressively the marking function responds to congestion) on queue overflow probability for uncontrolled real-time flows. First, we derive lower and upper bounds on the queue overflow probability at a router of a single bottleneck system. Using this, we quantify the trade-off between stability for controlled flows and queue overflow probability for uncontrolled real-time flows as a function of marking elasticity. Next, we compare the capacity required at a router with only FIFO scheduling versus a router with priority scheduling (priority given to the real-time flows) for supporting a given queue overflow probability. We quantify the scheduling-gain of priority scheduling over FIFO scheduling, as a function of marking elasticity. We show that this scheduling gain decreases with more elastic marking functions.

A Large Deviations Analysis of Scheduling in Wireless Networks

In this correspondence, we consider a cellular network consisting of a base station and N receivers. The channel states of the receivers are assumed to be identical and independent of each other. The goal is to compare the throughput of two different scheduling policies (a queue-length-based (QLB) policy and a greedy policy) given an upper bound on the queue overflow probability or the delay violation probability. We consider a multistate channel model, where each channel is assumed to be in one of L states. Given an upper bound on the queue overflow probability or an upper bound on the delay violation probability, we show that the total network throughput of the (QLB) policy is no less than the throughput of the greedy policy for all N. We also obtain a lower bound on the throughput of the (QLB) policy. For sufficiently large N, the lower bound is shown to be tight, strictly increasing with N, and strictly larger than the throughput of the greedy policy. Further, for a simple multistate channel model-ON-OFF channel, we prove that the lower bound is tight for all N

Interference-aware fair rate control in wireless sensor networks

In a wireless sensor network of N nodes transmitting data to a single base station, possibly over multiple hops, what distributed mechanisms should be implemented in order to dynamically allocate fair and efficient transmission rates to each node? Our interferenceaware fair rate control (IFRC) detects incipient congestion at a node by monitoring the average queue length, communicates congestion state to exactly the set of potential interferers using a novel low-overhead congestion sharing mechanism, and converges to a fair and efficient rate using an AIMD control law. We evaluate IFRC extensively on a 40-node wireless sensor network testbed. IFRC achieves a fair and efficient rate allocation that is within 20-40% of the optimal fair rate allocation on some network topologies. Its rate adaptation mechanism is highly effective: we did not observe a single instance of queue overflow in our many experiments. Finally, IFRC can be extended easily to support situations where only a subset of the nodes transmit, where the network has multiple base stations, or where nodes are assigned different transmission weights.

TCP BaLDE for Improving TCP Performance over Heterogeneous Networks

Network congestion and random errors of wireless link are two well-known noteworthy parameters which degrade the TCP performance over heterogeneous networks. We put forward a novel end-to-end TCP congestion control mechanism, namely TCP BaLDE (Bandwidth and Loss Differentiation Estimate), in which the TCP congestion control categorizes the reason of the packet loss by estimating loss differentiation in order to control the packet transmission rate appropriately. While controlling transmission rate depends on the available bandwidth estimation which is apprehended by the bandwidth estimation algorithm when the sender receives a new ACK with incipient congestion signal, duplicates ACKs or is triggered by retransmission timeout event. Especially, this helps the sender to avoid router queue overflow by opportunely entering the congestion avoidance phase. In simulation, we experimented under numerous different network conditions. The results show that TCP BaLDE can achieve robustness in aspect of stability, accuracy and rapidity of the estimate in comparison with TCP Westwood, and tolerate ACK compression. It can achieve better performance than TCP Reno and TCP Westwood. Moreover, it is fair on bottleneck sharing to multiple TCP flows of the same TCP version, and friendly to existing TCP version.

Numerical analysis of packet merge technique: The QoS improvement and the application domain

The packet merge technique combines packets and lowers the packet rate to improve the Quality of Service (QoS) degradation of a network caused by the increase in the delay time and packet losses due to a longer router queue and buffer overflow caused by the high packet rate traffic. However, combining the packets is accompanied by an increase in the waiting delays due to waiting for succeeding packets and transmission delay due to the larger packet size. Therefore, this situation becomes a new source of QoS degradation. There is a trade-off relationship between the improvement by this technique and the delays generated by the technique. Suitable applications and parameter settings are important to improve the QoS. In this paper, we derive the delay time distribution and the packet loss rate of the received packets passing through a network applying this packet merge technique, and describe the characteristics of this technique and the application domain. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 89(7): 35–48, 2006; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/ecja.20253

Efficient simulation of buffer overflow probabilities in jackson networks with feedback

Consider a Jackson network that allows feedback and that has a single server at each queue. The queues in this network are classified as a single ‘target’ queue and the remaining ‘feeder’ queues. In this setting we develop the large deviations limit and an asymptotically efficient importance sampling estimator for the probability that the target queue overflows during its busy period, under some regularity conditions on the feeder queue-length distribution at the initiation of the target queue busy period. This importance sampling distribution is obtained as a solution to a non-linear program. We especially focus on the case where the feeder queues, at the initiation of the target queue busy period, have the steady state distribution corresponding to these instants. In this setting, we explicitly identify the importance sampling distribution when the feeder queue service rates exceed a specified threshold. We also relate our work to the existing large deviations literature to develop a perspective on successes and limitations of our results.

Erratum: A Stochastic Modeling Approach to Real-Time Prediction of Queue Overflows

Queue overflow is a critical issue in developing queue prediction technologies for applications in Advanced Transportation Management System (ATMS). Conventional queue prediction methods, however, are limited to incident-free queue length prediction where traffic arrivals can be readily obtained using detectors. Despite the problems posed by queue overflow, studies addressing queue-overflow issues, or for predicting queue overflows beyond detectors, appear inadequate. This paper describes an advanced methodology which uses a stochastic system modeling approach and random processes for predicting queue lengths beyond detectors in real time. Lane changing is taken into account in developing the queue-overflow prediction model because lane changing accompanies queue overflow in most cases. A discrete-time, nonlinear stochastic system is specified for modeling the queues and lane changes beyond detectors during queue-overflow occurrence. The noise terms of the recursive equations of the model account for the effects of queues and a variety of arriving volumes on vehicular lane-changing maneuvers during queue-overflow occurrence. The unknown traffic arrivals beyond detectors are predicted employing random processes. In addition, a recursive estimation algorithm for predicting real-time queue overflows is developed utilizing the extended Kalman filtering technique. Preliminary test results indicate that the proposed methodology is promising for real-time prediction of queue overflows. The predicted queue overflows can be used not only in understanding the phenomenon of lane traffic patterns during queue-overflow occurrence, but also in developing related advanced technologies such as real-time road traffic congestion control and management systems.

A Stochastic Modeling Approach to Real-Time Prediction of Queue Overflows

Queue overflow is a critical issue in developing queue prediction technologies for applications in Advanced Transportation Management System (ATMS). Conventional queue prediction methods, however, are limited to incident-free queue length prediction where traffic arrivals can be readily obtained using detectors. Despite the problems posed by queue overflow, studies addressing queue-overflow issues, or for predicting queue overflows beyond detectors, appear inadequate. This paper describes an advanced methodology which uses a stochastic system modeling approach and random processes for predicting queue lengths beyond detectors in real time. Lane changing is taken into account in developing the queue-overflow prediction model because lane changing accompanies queue overflow in most cases. A discrete-time, nonlinear stochastic system is specified for modeling the queues and lane changes beyond detectors during queue-overflow occurrence. The noise terms of the recursive equations of the model account for the effects of queues and a variety of arriving volumes on vehicular lane-changing maneuvers during queue-overflow occurrence. The unknown traffic arrivals beyond detectors are predicted employing random processes. In addition, a recursive estimation algorithm for predicting real-time queue overflows is developed utilizing the extended Kalman filtering technique. Preliminary test results indicate that the proposed methodology is promising for real-time prediction of queue overflows. The predicted queue overflows can be used not only in understanding the phenomenon of lane traffic patterns during queue-overflow occurrence, but also in developing related advanced technologies such as real-time road traffic congestion control and management systems.

Hybrid technique for analysis of multiplexed power-law traffic in broadband networks

Recent research has shown that the simulation of traffic in many broadband network scenarios involves excessive time consumption, owing to the power-law nature of much of this traffic. As an alternative, analysis will often yield systems of equations that are prohibitively complex to solve. Thus, there is growing interest in combinations of simulation and analysis, i.e. hybrid solutions. Here, a hybrid technique is developed for the evaluation of queue state and overflow probabilities when buffering multiple ON/OFF sources, where ON and OFF periods are Pareto distributed. It is shown that this technique allows these probabilities to be predicted accurately, while avoiding insoluble systems of equations and excessively long simulations.