Predictive Congestion Control Research Articles

Optimization-Based Predictive Congestion Control for the Tor Network: Opportunities and Challenges

Based on the principle of onion routing, the Tor network achieves anonymity for its users by relaying user data over a series of intermediate relays. This approach makes congestion control in the network a challenging task. As of this writing, this results in higher latencies due to considerable backlog as well as unfair data rate allocation. In this article, we present a concept study of PredicTor, a novel approach to congestion control that tackles clogged overlay networks. Unlike traditional approaches, it is built upon the idea of distributed model predictive control, a recent advancement from the area of control theory. PredicTor is tailored to minimizing latency in the network and achieving max-min fairness. We contribute a thorough evaluation of its behavior in both toy scenarios to assess the optimizer and complex networks to assess its potential. For this, we conduct large-scale simulation studies and compare PredicTor to existing congestion control mechanisms in Tor. We show that PredicTor is highly effective in reducing latency and realizing fair rate allocations. In addition, we strive to bring the ideas of modern control theory to the networking community, enabling the development of improved, future congestion control. Thus, we demonstrate benefits and issues alike with this novel research direction.

Sliding Mode-based Predictive Congestion Control in a Satellite Space Information Transmission Network

Sliding Mode-based Predictive Congestion Control in a Satellite Space Information Transmission Network

Predictive Congestion Control based on Collaborative Information Sharing for Vehicular Ad hoc Networks

Traffic jams are an essential and continuous challenge in our cities, responsible for socioeconomic and environmental concerns and an ambitious traffic jams management agenda is urgent. The distributed solutions in the literature for Traffic Management Systems (TMS) are heavily based on beacon messages or proactive communication protocols to share vehicular traffic information among vehicles. Thus, these solutions are not scalable when the number of vehicles increases in the network — when there are traffic jams. To overcome these problems, we propose a new VANET-based traffic management system named CoNeCT: Predictive Congestion Control based on Collaborative Information Sharing for Vehicular Ad hoc Networks. CoNeCT’s primary goal is to support vehicles’ collaboration in analyzing, predicting, and managing congestion. The proposed system was designed to decrease the number of messages by using a novel road segment load assessment that improves traffic flow classification. Vehicles aware of traffic conditions share it with their neighbors, and they can also request traffic views whenever necessary. Additionally, vehicles can detect significant traffic variations and predict future traffic conditions to improve roads’ overall traffic conditions, mitigating the congestion before it arises. Results obtained from an extensive performance analysis show CoNeCT’s ability to reduce traffic congestion with a low impact on the wireless communication medium, outperforming the state-of-art systems.

Nonlinear Model Predictive Congestion Control for Networks

This paper addresses congestion control for networks. The main focus is the modeling and compensation of nonlinear disturbance in the network, which is seldom considered in the literature. A new congestion control method is presented to compensate for the effects of nonlinear disturbance, uncertainty, time-varying delay, and input constraint. A state feedback congestion controller is designed via model predictive control approach. The stability of the closed-loop system is analyzed by using Lyapunov-Krasovskii functional. The effectiveness and feasibility of the proposed controller are verified by simulation results.

A Route Weight Assignment Method for Predictive Congestion Control in WSN

A Route Weight Assignment Method for Predictive Congestion Control in WSN

Energy‐efficient predictive congestion control for wireless sensor networks

In recent years, wireless sensor networks have become an emerging technology in industries, environment monitoring and health care monitoring systems and so on. However, sensor node is a resource-constrained device in terms of memory, bandwidth and energy. These constraints impose congestion in the network, leading to large number of packet drops, low throughput and significant wastage of energy because of retransmission. This study presents a new approach for predicting congestion using probabilistic method, and controlling congestion using new rate control methods. The probabilistic approach used for the prediction of occurrence of congestion in a node is developed using data traffic and buffer occupancy. The rate control method uses rate allocation schemes, namely, rate reduction (RR), rate regulation (RRG) and split protocol (SP) to improve throughput and to reduce packet drops. In addition, an energy-efficient routing which finds the best forwarding node for data transmission is also proposed. Simulation results are compared with decentralised predictive congestion control (DPCC). The results show that the proposed method indeed reduces congestion and energy consumption, and improves the performance.

A probabilistic approach for predictive congestion control in wireless sensor networks

Any node in a wireless sensor network is a resource constrained device in terms of memory, bandwidth, and energy, which leads to a large number of packet drops, low throughput, and significant waste of energy due to retransmission. This paper presents a new approach for predicting congestion using a probabilistic method and controlling congestion using new rate control methods. The probabilistic approach used for prediction of the occurrence of congestion in a node is developed using data traffic and buffer occupancy. The rate control method uses a back-off selection scheme and also rate allocation schemes, namely rate regulation (RRG) and split protocol (SP), to improve throughput and reduce packet drop. A back-off interval selection scheme is introduced in combination with rate reduction (RR) and RRG. The back-off interval selection scheme considers channel state and collision-free transmission to prevent congestion. Simulations were conducted and the results were compared with those of de- centralized predictive congestion control (DPCC) and adaptive duty-cycle based congestion control (ADCC). The results showed that the proposed method reduces congestion and improves performance.

Cross-Layer Active Predictive Congestion Control Protocol for Wireless Sensor Networks

In wireless sensor networks (WSNs), there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC) for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node's neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks.

TCP modelling and predictive congestion control

This paper presents a comparative analysis of some models recently proposed in literature to represent the TCP protocol and its congestion control algorithm pointing out advantages and drawbacks. The comparative analysis also considers the results obtained with the NS-2 network'simulator to validate the different models. From this analysis a model is selected for control purposes and a generalized predictive controller is proposed to perform the TCP congestion control mechanism. The proposed controller can cope with the dead time of the process and handle the process constraints. Some simulations using NS-2 are used to show the good properties of the proposed model and controller in several cases such as variations in the time delay and communication bandwidth.

Predictive Congestion Control Protocol for Wireless Sensor Networks

Available congestion control schemes, for example transport control protocol (TCP), when applied to wireless networks, result in a large number of packet drops, unfair scenarios and low throughputs with a significant amount of wasted energy due to retransmissions. To fully utilize the hop by hop feedback information, this paper presents a novel, decentralized, predictive congestion control (DPCC) for wireless sensor networks (WSN). The DPCC consists of an adaptive flow and adaptive back-off interval selection schemes that work in concert with energy efficient, distributed power control (DPC). The DPCC detects the onset of congestion using queue utilization and the embedded channel estimator algorithm in DPC that predicts the channel quality. Then, an adaptive flow control scheme selects suitable rate which is enforced by the newly proposed adaptive backoff interval selection scheme. An optional adaptive scheduling scheme updates weights associated with each packet to guarantee the weighted fairness during congestion. Closed-loop stability of the proposed hop-by-hop congestion control is demonstrated by using the Lyapunov-based approach. Simulation results show that the DPCC reduces congestion and improves performance over congestion detection and avoidance (CODA) [3] and IEEE 802.11 protocols.

Adaptive predictive congestion control of high-speed ATM networks

This paper proposes an auto regressive moving average (ARMAX)-based adaptive control methodology to prevent congestion in high-speed asynchronous transfer mode (ATM) networks. An adaptive controller is developed to control traffic where sources adjust their transmission rates in response to the feedback information from the network switches. Specifically, the buffer dynamics at a given switch is modeled as a nonlinear discrete-time system and an ARMAX controller is designed so as to predict the explicit values of the transmission rates of the sources so as to prevent congestion. Tuning methods are provided for the unknown coefficients of the ARMAX model to estimate the unpredictable and statistically fluctuating network traffic. Mathematical analysis is given to demonstrate the stability of the closed-loop system so that a desired quality of service (QoS) can be guaranteed. The QoS is defined in terms of cell loss ratio (CLR), transmission delay and buffer utilization. We derive design rules mathematically for selecting the parameters of the ARMAX algorithm such that the desired performance is guaranteed during congestion and potential tradeoffs are shown. Simulation results are provided to justify the theoretical conclusions for multiple source/single switch scenarios using both ON/OFF and MPEG data. The performance of the proposed congestion control scheme is also evaluated in the presence of feedback delays for robustness considerations.

Predictive congestion control of ATM networks: multiple sources/single buffer scenario

This paper proposes a neural network (NN)-based adaptive control methodology to prevent congestion in high-speed asynchronous transfer mode (ATM) networks. The buffer dynamics at the switch is modeled as a nonlinear discrete-time system and a NN-based predictive controller is designed to predict the explicit values of the transmission rates of the sources so as to prevent congestion. Tuning methods are provided for the NN weights to estimate the unpredictable and statistically fluctuating network traffic. Mathematical analysis is given to demonstrate the stability of the closed-loop system so that a desired quality of service (QoS) can be guaranteed. The QoS is defined in terms of cell loss ratio (CLR) and latency. We derive design rules mathematically for selecting the NN tuning algorithm such that the desired performance is guaranteed during congestion and potential tradeoffs are shown. Simulation results are provided to justify the theoretical conclusions for single source/single switch scenario using ON/OFF data. Finally, comparison studies are also included to show the effectiveness of the proposed method over conventional rate-based and thresholding techniques during simulated congestion.

Estimation and Prediction-Based Connection Admission Control in Broadband Satellite Systems

We apply a “sliding-window” Maximum Likelihood (ML) estimator to estimate traffic parameters of On-Off source and develop a method for estimating stochastic predicted individual cell arrival rates. Based on these results, we propose a simple Connection Admission Control (CAC) scheme for delay sensitive services in broadband onboard packet switching satellite systems. The algorithms are motivated by the limited onboard satellite buffer, the large propagation delay, and low computational capabilities inherent in satellite communication systems. We develop an algorithm using the predicted individual cell loss ratio instead of using steady state cell loss ratios. We demonstrate the CAC benefits of this approach over using steady state cell loss ratios as well as predicted total cell loss ratios. We also derive the predictive saturation probability and the predictive cell loss ratio and use them to control the total number of connections. Predictive congestion control mechanisms allow a satellite network to operate in the optimum region of low delay and high throughput. This is different from the traditional reactive congestion control mechanism that allows the network to recover from the congested state. Numerical and simulation results obtained suggest that the proposed predictive scheme is a promising approach for real time CAC.

A predictive congestion control policy for broadband integrated wide area networks

In this paper, we present a predictive congestion control policy for transporting data traffic in a high-speed wide-area network, in the presence of real-time traffic, such as voice and video traffic. The policy is based on information exchange between adjacent nodes of the network. The evolution of the buffer content at a node is determined by the real-time traffic arrival process, the data traffic from upstream nodes, and the ability of the downstream nodes to absorb the data traffic. The predictive policy attempts to predict the buffer content of a node one round trip delay into the future. Based on this prediction, in each time period, a node informs its immediate upstream neighbors, of the maximum rate at which they can transmit data traffic. This rate must be low enough to keep losses low and high enough to keep the throughput high. We characterize the real-time traffic by its mean rate, its variance and the correlation. We study the performance of our scheme over a wide range of real-time traffic parameter values. We investigate the effect of higher link speeds, and larger propagation delays on the performance of the network. We study the unfairness that results when sources are at different distances from a common access node, and propose a solution to overcome the bias towards the near source. We also compare the predictive policy to other end-to-end control policies. Our investigation shows that the predictive control policy performs significantly better than the static or adaptive end-to-end rate control policies.