Rate-based Congestion Control Research Articles

A Rate-Based Congestion Control Method for NDN Using Sparse Explicit Rate Notification and AIMD-Based Rate Adjustment

A Rate-Based Congestion Control Method for NDN Using Sparse Explicit Rate Notification and AIMD-Based Rate Adjustment

Fuzzy Congestion Control and Avoidance for CoAP in IoT Networks

In Internet of Things (IoT) networks, congestion is growing with the increasing number of devices, and a large amount of collected data must be transferred. Congestion control is one of the most significant challenges for such networks. The Constrained Application Protocol (CoAP) has been adopted for the IoT to satisfy the demand for smart applications. However, CoAP uses a basic congestion control algorithm that operates only when congestion occurs. Thus, the basic CoAP and most similar loss-based congestion control schemes have remaining issues for burst data transfer in dynamic network environments. This paper proposes a novel rate-based congestion control scheme using fuzzy control for CoAP, called FuzzyCoAP. We use the round-trip time gradient and bottleneck bandwidth gradient as inputs for FuzzyCoAP to infer the degree of congestion. FuzzyCoAP uses this indicator to predict early congestion and adjusts the sending rate to avoid congestion. FuzzyCoAP uses the congestion degree to update the variable RTO for retransmissions. On the other hand, FuzzyCoAP dynamically checks for the available bandwidth to gain high performance for burst data transfer. Various simulation experiments have demonstrated the feasibility of the FuzzyCoAP in different traffic scenarios. We compared the proposed scheme with representative loss-based CoAP schemes, that is, the basic CoAP. The simulation results proved that FuzzyCoAP provides high performance in terms of delay, throughput, loss rate, and retransmissions compared with the basic CoAP.

A survey and taxonomy of congestion control mechanisms in wireless network on chip

Wireless network on chip (WiNoC) has been proposed as a promising solution for on-chip interconnection network due to high scalability, high bandwidth, and low latency. However, the variations of traffic pattern distribution and data flow lead to congestion in wireless interfaces-equipped routers (WRs). Congestion is one of the main challenges in emerging WiNoCs that can reduce network performance. In recent years, various researches have been proposed to decrease congestion in WiNoCs. However, this paper presents a comprehensive survey of the significant congestion control mechanisms in WiNoCs. The available schemes are classified into six categories, includeing hardware resources-based congestion control, congestion-aware routing algorithms, medium access control protocol,congestion-aware architectures, rate-based congestion control, and application-mapping with task-migration techniques. The goal of this survey is to highlight the characteristics and inherent constraints of congestion control mechanisms in a novel approach that can help researchers for designing efficient congestion control scheme.

Rate Based Congestion Control for Wireless Links in Information Centric Network

Information-centric networking (ICN) is the prominent network architecture with the features of name based forwarding and in-network caching. These features enables ICN to provide solutions to all demands of the emerging networks. Among the proposed hop by hop congestion control schemes in ICN, almost all schemes assume the available link capacity as known and fixed which is not true for wireless links. Here we propose dynamic link capacity estimation for wireless links using kalman filter and each node maintains the data rate value estimated. Consumer forwards interest with initial data rate, each hop updates estimated data rate in outgoing data packets proactively, then consumer adjusts the data rate according to new value received.

Neural network model as Internet of Things congestion control using PID controller and immune-hill-climbing algorithm

Congestion control of traffic flow in the Internet of Things (IoT) and fog computing is very essential to achieve certain Quality of Service (QoS) in critical applications. The huge data and the limited resources in IoT and cloud system ascertain the real need for efficient congestion control scheme especially in real-time fog computing applications. In this paper, a Proportional-Integrator-Differentiator (PID) controller is used and tuned in a rate-based congestion control scheme to be implemented for IoT applications data communication. The tuning strategy is based on reformulating the problem to be as an optimization problem with a compact fitness function reflecting the control requirements. The main contribution of this paper is to adopt an enhanced hybrid immune-hill-climbing immune algorithm as the tuning process. Then, an Artificial Neural Network (ANN) is built and trained in order to be used instead of the repetitive and exhausting retuning. The proposed method works in a cascaded manner; the immune algorithm is used for coarse tuning and then the hill climbing algorithm is triggered for fine-tuning. For the purpose of comparison, a simplified network model is built and simulated using two control methods, namely the control scheme and the well-known single-bit indicator scheme. The experimental results prove that the PID controller tuned by the immune-hill-climbing algorithm is superior in terms of the stability of both, buffer occupancy in the switch and the source rate. Both the transient behavior and the steady-state response for the PID controller are shown to be much better than the single-bit indicator scheme. Consequently, the proposed control scheme outperforms the single-bit indicator in terms of link utilization, buffer utilization, packet drop and robustness against any burst change in fog and cloud computing systems. It is shown that using trained ANN instead of the repetitive and exhausting retuning is improving the response time.

Energy efficient optimized rate based congestion control routing in wireless sensor network

A wireless sensor network is designed to facilitate various real time applications, constituting a wide range of sensor nodes. In order to provide energy efficient transmissions, a novel congestion control mechanism is proposed on optimized rate. Here, the rate-based congestion control algorithm based on cluster routing is introduced to reduce energy consumption throughout the network. Rate control process reduces the end to end delay to improve network life time for large simulation period. Initially, nodes are clustered by the hybrid K-means and Greedy best first search algorithms. After that, the rate control is performed using firefly optimization strategy which is suitable for high packet delivery ratio. Finally, packets are sent with maximum throughput using Ant Colony Optimization-based routing. The simulation is performed on the MATLAB simulation platform. Finally, performances are evaluated with respect to, average delay in end to end node, delivery ratio of packets, throughput, energy efficiency, energy consumption and reliability.

Sustainability Analysis for Fog Nodes With Renewable Energy Supplies

There is a growing interest in the use of renewable energy sources to power fog networks in order to mitigate the detrimental effects of conventional energy production. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a fog node powered by renewable energy sources is studied. We develop a generic analytical model to study the energy sustainability of fog nodes powered by renewable energy sources, by generalizing the leaky bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. Based on the closed-form solutions of energy buffer analysis, i.e., the energy depletion probability and mean energy length, we study the energy sustainability in two special but real-happening scenarios. The experimental results show that with proper design the leaky bucket model effectively reflects the energy sustainability of data traffic in fog networks. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics in fog networks.

VIRAL: coupling congestion control with fair video quality metric

Video streaming is often carried out by congestion controlled transport protocols to preserve network sustainability. However, the success of the growth of such non-live video flows is linked to the user quality of experience. Thus, one possible solution is to deploy complex quality of service systems inside the core network. Another possibility would be to keep the end-to-end principle while making aware transport protocols of video quality rather than throughput. The objective of this article is to investigate the latter by proposing a novel transport mechanism which targets video quality fairness among video flows. Our proposal, called VIRAL for virtual rate-quality curve, allows congestion controlled transport protocols to provide fairness in terms of both throughput and video quality. VIRAL is compliant with any rate-based congestion control mechanisms that enable a smooth sending rate for multimedia applications. Implemented inside TFRC a TCP-friendly protocol, we show that VIRAL enables both intra-fairness between video flows in terms of video quality and inter-fairness in terms of throughput between TCP and video flows.

P4QCN: Congestion Control Using P4-Capable Device in Data Center Networks

Modern data centers aim to offer very high throughput and ultra-low latency to meet the demands of applications such as online intensive services. Traditional TCP/IP stacks cannot meet these requirements due to their high CPU overhead and high-latency. Remote Direct Memory Access (RDMA) is an approach that can be designed to meet this demand. The mainstream transport protocol of RDMA over Ethernet is RoCE (RDMA over Converged Ethernet), which relies on Priority Flow Control (PFC) within the network to enable a lossless network. However, PFC is a coarse-grained protocol which can lead to problems such as congestion spreading, head-of-the-line blocking. A congestion control protocol that can alleviate these problems of PFC is needed. We propose a protocol, called P4QCN for this purpose. P4QCN is a congestion control scheme for RoCE and it is an improved Quantized Congestion Notification (QCN) design based on P4, which is a flow-level, rate-based congestion control mechanism. P4QCN extends the QCN protocol to make it compatible with IP-routed networks based on a framework of P4 and adopts a two-point algorithm architecture which is more effective than the three-point architecture used in QCN and Data Center QCN(DCQCN). Experiments show that our proposed P4QCN algorithm achieves the expected performance in terms of latency and throughput.

Avoiding Excessive Rate Reduction in Rate Based Congestion Control for Named Data Networking

Avoiding Excessive Rate Reduction in Rate Based Congestion Control for Named Data Networking

Rate-Based End-to End Closed Loop Control for ABR Traffic Management

ATM has one common feature which guarantees its success, namely the possibility to transport any ATM service, irrespective of its characteristics such as the bit rate, its quality requirements or its bursty nature. ATM also has high flexibility in allocate the bandwidth, and thus allows more calls to enter the network. Unfortunately, this advantage has a negative consequences. ATM presents congestion problems in the network as well as in the end terminals. This research describes about congestion control mechanism that will be applied for Available Bit Rate (ABR) Service in ATM networks, namely, the Rate-Based Congestion Control Scheme. This method supports end-to-end closed-loop control and has been indentified as the most appropriate for the support of this service by ATM Forum. ABR Service is a new ATM Service category. This kind of service automatically and dynamically allocates the available bandwidth to users by controlling the traffic flow based on feedback information. The source increases or decreases the source rate of cells transmission based on the information in the feedback Resource Management (RM) cell it receives. This method minimizes the duties of intermediate nodes and the destination node, so it is expected the source will receive the feedback information faster.

TCP-Gvegas with prediction and adaptation in multi-hop ad hoc networks

TCP Vegas performance can be improved since its rate-based congestion control mechanism could proactively avoid possible congestion and packet losses in multi-hop ad hoc networks. Nevertheless, Vegas cannot make full advantage of available bandwidth to transmit packets since incorrect bandwidth estimates may occur due to frequent topology changes caused by node mobility. This paper proposes an improved TCP Vegas based on the grey prediction theory, named TCP-Gvegas, for multi-hop ad hoc networks, which has the capability of prediction and self-adaption, as well as three enhanced aspects in the phase of congestion avoidance. The lower layers' parameters are considered in the throughput model to improve the accuracy of theoretical throughput. The prediction of future throughput based on grey prediction is used to promote the online control. The optimal exploration method based on Q-Learning and Round Trip Time quantizer are applied to search for the more reasonable changing size of congestion window. Besides, the convergence analysis of grey prediction by using the Lyapunov's second method proves that a shorter input data length of prediction implies a faster convergence rate. The simulation results show that the TCP-Gvegas achieves a substantially higher throughput and lower delay than Vegas in multi-hop ad hoc networks.

Fuzzy Logic based High Speed Network Congestion Control

In this paper, we describe the formatting guidelines for IJCA Journal Submission. Congestion is the problem that occurs due to saturation of network resources. Still the implementation of traditional congestion control algorithms such as OSI Layer 4 Transmission Control Protocol/ Internet Protocol (TCP / IP), due Objected Oriented congestion remains a critical issue in Local Network, ATM networks and SONET. Fuzzy Logic is applied to resolve the network traffic control problem as medium of networks are too difficult using traditional control system theory. Fuzzy Logic based congestion control good result the traditional methods in various cases. It is the first time that an explicit rate-based congestion control system designed with the fuzzy logic control is proved globally asymptotically stable. This Paper is a review of Fuzzy Logic and Neural-Fuzzy based techniques that applied to deal with congestion. Fuzzy Logic based Congestion Controller is a model free controller that utilizes qualitative reasoning to implement non-linear control functions efficiently.

R2C2

Rack-scale computers, comprising a large number of micro-servers connected by a direct-connect topology, are expected to replace servers as the building block in data centers. We focus on the problem of routing and congestion control across the rack's network, and find that high path diversity in rack topologies, in combination with workload diversity across it, means that traditional solutions are inadequate. We introduce R2C2, a network stack for rack-scale computers that provides flexible and efficient routing and congestion control. R2C2 leverages the fact that the scale of rack topologies allows for low-overhead broadcasting to ensure that all nodes in the rack are aware of all network flows. We thus achieve rate-based congestion control without any probing; each node independently determines the sending rate for its flows while respecting the provider's allocation policies. For routing, nodes dynamically choose the routing protocol for each flow in order to maximize overall utility. Through a prototype deployed across a rack emulation platform and a packet-level simulator, we show that R2C2 achieves very low queuing and high throughput for diverse and bursty workloads, and that routing flexibility can provide significant throughput gains.

Modeling of rate-based congestion control schemes in cognitive radio sensor networks

Performance evaluation of transport layer protocols in cognitive radio sensor networks (CRSNs) is useful to provide quality-of-service for real-time reliable applications. This paper develops an analytical framework to model the steady-state sending rate of collecting cognitive radio (CR) sensors in rate-based generic additive-increase multiplicative-decrease (AIMD) and additive-increase additive-decrease (AIAD) congestion control schemes. Evolution process of sending rate is modeled by a discrete time Markov chain (DTMC) in the terms of queue length. We model the queue length distribution of a CR node by a semi-Markov chain (SMC) with assuming general probability density functions (PDFs) of input rate and attainable sending rate of the node. These PDFs are derived based on the parameters of MAC and physical layers and CRSN configuration. The proposed models are verified through various simulations.

On the Optimality of Generic Rate-Based AIMD and AIAD Congestion Control Schemes in Cognitive Radio Sensor Networks

Investigating the optimality and the modeling of congestion control schemes is crucial in order to provide quality of service (QoS) for various applications in cognitive radio sensor networks (CRSNs). This paper develops an analytical framework to study the optimality of rate-based generic AIMD and AIAD congestion control schemes. In this way, a congestion model is introduced to describe the congestion behavior of CRSNs. A semi-Markov chain (SMC) is proposed to model the steady-state sending rate distribution of source nodes based on the congestion model. The optimality of generic AIMD and AIAD, based on the proposed models, is analyzed in order to maximize the defined rate-congestion ratio (RCR). The analytical results are verified through various NS2-based simulations in CRSNs.

AeroMTP: A fountain code-based multipath transport protocol for airborne networks

Airborne networks (ANs) are special types of ad hoc networks that can be used to enhance situational awareness, flight coordination and flight efficiency in civil and military aviation. Compared to ground networks, ANs have some unique attributes including high node mobility, frequent topology changes, mechanical and aerodynamic constrains, strict safety requirements and harsh communication environment. Thus, the performance of conventional transmission control protocol (TCP) will be dramatically degraded in ANs. Aircraft commonly have two or more heterogeneous network interfaces which offer an opportunity to form multiple communication paths between any two nodes in ANs. To satisfy the communication requirements in ANs, we propose aeronautical multipath transport protocol (AeroMTP) for ANs, which effectively utilizes the available bandwidth and diversity provided by heterogeneous wireless paths. AeroMTP uses fountain codes as forward error correction (FEC) codes to recover from data loss and deploys a TCP-friendly rate-based congestion control mechanism for each path. Moreover, we design a packet allocation algorithm based on optimization to minimize the delivery time of blocks. The performance of AeroMTP is evaluated through OMNeT++ simulations under a variety of test scenarios. Simulations demonstrate that AeroMTP is of great potential to be applied to ANs.

Improving the Incast Performance of Datacenter TCP by Using Rate-Based Congestion Control

Improving the Incast Performance of Datacenter TCP by Using Rate-Based Congestion Control

A Rate based Congestion Control Mechanism Using Fuzzy Controller in MANETs

The traditional congestion control mechanism TCP, performs very poorly in MANETs Because there are a number of new challenges such as wireless link error, medium contention and frequent route failures in this kind of networks. In this paper, we propose a fuzzy adhoc rate-based congestion control (FARCC) to enhance the efficiency of network in MANETs. In FARCC, we use a rate-based transmission scheme using two fuzzy controller of zero order Takagi Sugeno Kang (TSK) model to congestion detection and congestion control. The FARCC sender adjusts data rate by receiving a feedback packet from FARCC destination. NS2-based simulation results show that FARCC outperforms ITP and ATP to achieve, in terms of throughput and fair resource allocation in AdHoc networks under random topology.

Analysis of Rate Based Congestion Control Algorithms in Wireless Technologies

In this paper, the congestion control algorithms in wireless technolologies are analysed. It has been found that TCP VEGAS is better than other TCP variants for sending data and information due to better delivery fraction and average end to end delay. But it has a consistent window size for packet transmission. Congestion Avoidance can resolve Congestion effectively and has higher average throughput than slow start due to the ability to deal with random loss. Cross layer Congestion Control require significant power and memory corresponding to network bandwidth. So to overcome all these problems, here we analyse the performance of AIMD, TFRC and TCP congestion control protocol. After observing simulation results, it is found that GAIMD is better than TFRC in terms of throughput and TFRC is better than GAIMD in terms of smoothness.