SIRENS: An Explicit Notification Framework for Internet Congestion Control

Chapter 5 - Congestion Control in High-Speed Networks

We consider simple models of congestion control in high-speed networks, and develop diffusion approximations which could be useful for resource allocation. We first show that, if the sources are ON-OFF type with exponential ON and OFF times, then, under a certain scaling, the steady-state distribution of the number of active sources can be described by a combination of two appropriately truncated and renormalized normal distributions. For the case where the source arrival process is Poisson and the service times are exponential, the steady-state distribution consists of appropriately normalized and truncated Gaussian and exponential distributions. We then consider the case where the arrival process is a general renewal process with finite coefficient of variation and service-time distributions that are phase type, and show the impact of these distributions on the steady-state distribution of the number of sources in the system. We also establish an insensitivity to service-time distribution when the arrival process is Poisson. We use these results to relate the capacity of a bottleneck node to performance measures of interest for best effort traffic.

Unreliable transport protocol using congestion control for high-speed networks

This paper proposes an adaptive reinforcement co-learning method for solving congestion control problems on high-speed networks. Conventional congestion control scheme regulates source rate by monitoring queue length restricted to a predefined threshold. However, the difficulty of obtaining complete statistics on input traffic to a network. As a result, it is not easy to accurately determine the effective thresholds for high-speed networks. We proposed a simple and robust Co-learning Multi-agent Congestion Controller (CMCC), which consists of two subsystems: a long-term policy evaluator and a short-term rate selector incorporated with a co-learning reinforcement signal to solve the problem. The well-trained controllers can adaptively take correct actions to regulate source flow under time-varying environments. Simulation results showed the proposed approach can promote the system utilization and decrease packet losses simultaneously.

In this study, a fuzzy autoregressive (fuzzy-AR) model is proposed to describe the traffic characteristics of high-speed networks. The fuzzy-AR model approximates a nonlinear time-variant process with a combination of several linear local AR processes using a fuzzy clustering method. We propose that the use of this fuzzy-AR model has greater potential for congestion control of packet network traffic. The parameter estimation problem in fuzzy-AR modeling is treated by a clustering algorithm developed from actual traffic data in high-speed networks. Based on the adaptive AR-prediction model and queueing theory, a simple congestion control scheme is proposed to provide an efficient traffic management for high-speed networks. Finally, using the actual Ethernet-LAN packet traffic data, several examples are given to demonstrate the validity of this proposed method for high-speed network traffic control.

In this paper, we study the performance of utility maximization congestion control over multihop CSMA-based networks. We consider decoupled vs. joint design of congestion control and medium access and consider unmodified MAC protocols such as IEEE 802.11. Networks employing such MAC protocols incur flow starvation both without congestion control and with existing TCP-based congestion control. We develop a framework to study key issues in such networks that are not incorporated by prior models, yet are critical to the performance of congestion control algorithms. We study the role of data transmission capacity that is location dependent and, even more, unknown. We show that for the case of consistent channel state, a single globally optimal data transmission capacity does not exist. Moreover, for the case of inconsistent channel state that arises due to the carrier sense mechanism itself, a data transmission capacity that provides convergence to perfectly fair rates does not exist, i.e., the congestion control algorithm converges to incorrect rates. We study the impact of inter-node collaboration within a contention region, and show that collaboration can alleviate these problems and ensure convergence to fair rates. Finally, we compare the performance of congestion control in a collaborative network with the performance of TCP, and show that TCP starves some flows, whereas congestion control with collaboration removes starvation, provides significantly better fairness, and achieves 17% higher aggregate throughput.

This paper focuses on flow control in high-speed net-works. Each node in a network handles its local traffic flow only on the basis of the information it is aware of, but it is preferable that the decision-making of each node leads to high performance of the whole network. To this end, we investigate the relationship between the flow control mech-anism of each node and network performance. We consider the situation in which the capacity of a link in the network is changed but individual nodes are not aware of this. Then we investigate the stability and adaptability of the network performance when the capacity of a link is changed, and discuss an appropriate flow control model on the basis of simulation results. 1

We study link capacity allocation for a finite buffer system to transmit multimedia traffic. The queueing process is simulated with real video traffic. Two key concepts are explored in this study. First, the link capacity requirement at each node is essentially captured by its low-frequency input traffic (filtered at a properly selected cut-off frequency). Second, the low-frequency traffic stays intact as it travels through a finite-buffer system without significant loss. Hence, one may overlook the queueing process at each node for network-wide traffic flow in the low-frequency band. We propose a simple, effective method for link capacity allocation and network control using on-line observation of traffic flow in the low-frequency band. The study explores a new direction for measurement-based traffic control in high-speed networks. >

Information-centric networking (ICN) is expected to be a candidate for future internet architecture, and it supports features such as multicast that improves bandwidth utilization and transmission efficiency. However, multicast itself does not provide congestion control. When multiple multicast groups coexist, multicast traffic may exhaust all network resources, and cause network congestion and packet loss. Additionally, traditional IP multicast congestion control mechanisms cannot be directly applied to ICN architecture. Therefore, it is necessary to consider an effective congestion control mechanism for ICN multicast. This paper proposes a single-rate multicast congestion control mechanism, called SRMCC. It supports router-assisted awareness of the network congestion state and congestion control message aggregation. Moreover, the fair shared rate estimation method is innovatively proposed to achieve protocol fairness. Most importantly, it adjusts the rate according to different congestion states indicated by the queue occupancy ratio. By introducing a rate selection factor, it can achieve a balance between packet loss rate and throughput. Experimental results show that our proposal outperforms other mechanisms in throughput, packet loss rate, total bandwidth utilization, and overhead, and achieves protocol fairness and better TCP friendliness.

Maintaining the performance of reliable transport protocols, such as transmission control protocol (TCP), over wireless mesh networks (WMNs) is a challenging problem due to the unique characteristics of data transmission over WMNs. The unique characteristics include multi-hop communication over lossy and non-deterministic wireless mediums, data transmission in the absence of a base station, similar traffic patterns over neighboring mesh nodes, etc. One of the reasons for the poor performance of conventional TCP variants over WMNs is that the congestion control mechanisms in conventional TCP variants do not explicitly account for these unique characteristics. To address this problem, this paper proposes a novel artificial intelligence based congestion control technique for reliable data transfer over WMNs. The synergy with artificial intelligence is established by exploiting a carefully designed neural network (NN) in the congestion control mechanism. We analyze the proposed NN based congestion control technique in detail and incorporate it into TCP to create a new variant that we name as intelligent TCP or iTCP. We evaluate the performance of iTCP using both ns-2 simulations and real testbed experiments. Our evaluation results demonstrate that our proposed congestion control technique exhibits a significant improvement in total network throughput and average energy consumption per transmitted bit compared to the congestion control techniques used in other TCP variants.

In this chapter, layer-2 hop-by-hop congestion control mechanisms for wireless mesh networks are studied. First, background on development of congestion control as a research area is presented. Then, congestion is defined and the causes of congestion in a mesh network are identified. The steps and challenges for development of a congestion control protocol are explored by dividing congestion control mechanism into three main functions: congestion detection, signaling, and resolution. Stability and fairness in the context of rate control are studied, and global stability in the presence of delay is identified as the main challenge faced by hop-by-bop congestion control algorithms in multihop networks. The congestion control framework in the IEEE 802.11 draft standard on mesh networking is then presented. The chapter concludes with thoughts for practitioners and areas for future research in this field.

We present a control theoretic approach to the design of closed loop rate based flow control in high speed networks. The proposed control uses a dual proportional derivative controller, where the control parameters can be designed to ensure the stability of the control loop in a control theoretic sense, over a wide range of traffic patterns and propagation delays. We show how the control mechanism can be used to design a controller to support available bit rate (ABR) service based on feedback of explicit rates. We demonstrate the excellent transient and steady state performance of the controller through a number of examples.

This paper presents a novel Call Admission Control (CAC) scheme which adopts the neural network approach, namely Minimal Resource Allocation Network (MRAN) and its extended version EMRAN. Though the current focus is on the Call Admission Control (CAC) for Asynchronous Transfer Mode (ATM) networks, the scheme is applicable to most high-speed networks. As there is a need for accurate estimation of the required bandwidth for different services, the proposed scheme can offer a simple design procedure and provide a better control in fulfilling the Quality of Service (QoS) requirements. MRAN and EMRAN are on-line learning algorithms to facilitate efficient admission control in different traffic environments. Simulation results show that the proposed CAC schemes are more efficient than the two conventional CAC approaches, the Peak Bandwidth Allocation scheme and the Cell Loss Ratio (CLR) upperbound formula scheme. The prediction precision and computational time of MRAN and EMRAN algorithms are also investigated. Both MRAN and EMRAN algorithms yield similar performance results, but the EMRAN algorithm has less computational load.

The sharply increasing amount of data, which are transferred by the satellite network, requires the satellite network to provide quality‐of‐service (QoS). However, the upsurge in the data flow leads to the network congestion, impeding its ability to offer QoS. Congestion control mechanisms, deployed in the ground networks, have been thoroughly studied. But those deployed in the satellite networks have not been studied yet. As satellite networks are now important supplements to the ground backbone networks, this paper carefully analyzes the current challenges of developing the QoS‐oriented congestion control mechanism for satellite networks. On this basis, a consequent mechanism QMCC (QoS‐oriented mechanism of congestion control for satellite networks) is described in detail. Under that QMCC, the source nodes utilize an equation to compute the sending rate for each data flow; meanwhile, the intermediate nodes keep detecting real‐time package‐loss rates for timely adjustments. Simulation results indicate QMCC can provide superior congestion control performance, and raise network throughputs without reducing the QoS effects.

Fast-growing applications like streaming media make TCP a poor fit. Because the UDP protocol lacks congestion control, Equation-based and AIMD-based congestion control schemes have been two promising alternative to TCP for real-time multimedia streaming over the Internet. Past studies have shown that they are able to maintain throughput smoothness and achieve reasonable fairness with competing TCP flows in wireline networks. However, their behavior remains not clear in mobile ad hoc network (MANET). In this paper, we compare the general AIMD-based congestion control mechanism (GAIMD) with Equation-based congestion control mechanism (TFRC TCP-Friendly Rate control) over a wide range of MANET scenario, in terms of throughput fairness and smoothness. Our results show that TFRC and GAIMD (0.2, 0.125) are able to maintain throughput smoothness in MANET, but at the same time, they obtain less throughput than the competing TCP flows. Our results also show that TFRC changes its sending rate more smoothly than GAIMD (0.2, 0.125) does, but it gets the least throughput compares with TCP and GAIMD (0.2, 0.125).