Proposed Congestion Control Research Articles

Vivace-Distributed: A novel congestion control mechanism for JointCloud environments

As the customer base of cloud computing services continues to grow, the demand for providing cloud resources in an economic manner has become increasingly important. To incorporate multiple clouds, JointCloud has been proposed for sharing cloud services. However, efficient communication among clouds is essential to enable resource sharing in JointCloud, which poses a critical challenge. The existing congestion control mechanisms for JointCloud have been founded to be inadequate in sharing network resources effectively and fairly, resulting in performance shortcomings. The maximum achievable transmission rate is limited to only 50% of the bandwidth, and the minimum and maximum transmission rates for different clouds in JointCloud may have a difference of up to ten times in magnitude.We propose a novel distributed congestion control mechanism for the JointCloud environment, which leverages ideas from rich literature on distributed online learning. The proposed mechanism operates in a distributed manner, with an aggregative variable representing the network condition integrated into each sender’s utility function. Our theoretical analyses establish that the proposed mechanism achieves a unique Nash equilibrium, enabling fair resource allocation among all senders. Furthermore, our simulation evaluations demonstrate that the proposed mechanism outperforms the existing congestion control mechanism in JointCloud environment by achieving a 20% higher throughput in the presence of at least 1% random loss, a 50% higher throughput for short TCP flows, and a throughput ratio of 1 when sharing bandwidth with conventional mechanisms, which indicates a perfect TCP friendliness towards other mechanisms. In summary, our proposed congestion control mechanism is the first to work in a distributed manner, which improves the efficiency and fairness of the congestion control mechanism. The proposed mechanism has the potential to significantly enhance the performance of communication among clouds, making a noteworthy contribution to cloud resource sharing.

Modified Multipath Transmission Control Protocol Using Logarithmic Increase Under Long-Term Evolution and Wi-Fi Networks

Multipath transmission control protocol (MPTCP) has been proposed for devices with multiple network interfaces. This idea seems to be impossible for user implementation at the beginning of its proposal. However, the emergence of smartphones with multiple network access allows this idea to be implemented. This paper provides both theoretical and empirical throughput analysis of MPTCP under long term evolution (LTE) and Wi-Fi networks. First, a new modified congestion control is presented as an enhancement of standard TCP Reno using logarithmic function. Second, the existing MPTCP approaches like linked increase algorithm (LIA) and balanced LIA (BALIA) are discussed briefly. Finally, the experimental results under long term evolution (LTE) and Wi-Fi under software defined wireless networking (SDWN) are given along with the analysis. The experimental result shows that the proposed congestion control achieves higher average congestion window size. The BALIA MPTCP is shown to have higher throughput than that of the LIA MPTCP.

A Noncooperative Gaming Approach for Control Packet Transmission in 6TiSCH Network

The 6TiSCH communication architecture is widely used in Industrial Internet of Things (IIoT) to provide reliable, delay-bounded, and energy-efficient communication in multihop scenarios. However, the channel hopping feature and the resource allocation strategy of 6TiSCH minimal configuration (6TiSCH-MC) standard negatively impact the 6TiSCH network by increasing network formation time. 6TiSCH-MC allows only one cell (known as minimal cell) per slotframe to transmit control packets. When the number of joined nodes increases in the network, the formation time also increases because of the increasing congestion in the minimal cell. Furthermore, the existing works did not study the effect of transmission rates of all control packets together during 6TiSCH network formation. Therefore, in this work, a noncooperative game is formulated, for optimal transmission of control packets by the joined nodes. The obtained solution of the proposed game, using the Lagrange multiplier and Karush–Kuhn–Tucker (KKT) conditions, is used in the proposed congestion control scheme—game theory-based congestion control (GTCC). GTCC calculates the slotframe window (SW) size for every node to control the congestion in minimal cell without any signaling overhead. GTCC is validated using the analytical model as well as the FIT IoT-LAB testbed. The findings of both the analytical and testbed experiments show that GTCC significantly reduces the joining time and energy consumption of new nodes (i.e., pledges) as compared to previous benchmark schemes.

A hierarchical congestion control method in clustered internet of things

Internet of things (IoT) is a modern technology where data can be transmitted to any things (human, animal, or object) over communications networks, whether internet or intranet. Congestion occurs when the input data rate to the node was higher than the output data rate of node. Congestion control in computer network modulates traffic entry into a network in order to avoid congestive. This paper suggests a method for congestion control in the internet of things in two phases. The first phase is intra-cluster congestion control, which uses two parameters, congestion score (CS) and buffer empty space (BES), to congestion avoidance. In this phase based on these two parameters, nine states are defined to determine the congestion status of each node, and based on these 9 states the appropriate decision is made to the node. The second phase is inter-cluster congestion control. In this phase, after determining cluster head priority, the parameters of back-off timer (BFT), waiting time to receive acknowledgment \(({\text {WTTR}}_{\text {ACK}})\), sequence number (SEQ) and retransmission counter (RC) are used for congestion control. The proposed congestion control method is simulated by NS-2 software. A comparison between the performance of proposed method and conventional methods shows that applying proposed method results in a significant improvement in average congestion score (CS), packet lost rate, energy consumption and end-to-end delay.

A Distributed Congestion Control Strategy Using Harmonic Search Algorithm in Internet of Vehicles

Aiming at the diversified requirements of network application QoS (Quality of Service) in the terminal equipment of Internet of Vehicles, this paper proposes a distributed congestion control strategy based on harmony search algorithm and the Throughput Evaluation Priority Adjustment Model (TEPAM) to ensure real-time transmission of high-priority data messages related to security applications. Firstly, the channel usage rate is periodically detected and the congestion is judged; then, in order to minimize delay and delay jitter as the goal, harmony search algorithm is utilized to perform global search to obtain a better solution for the transmission range and transmission rate. Secondly, packet priority and the TEPAM are applied to indicate the sending right of each packet. The data message priority and throughput percentage factor are used to express the transmission weight of each data message. Besides, the real-time evaluation of path state in MPTCP is carried out by the batch estimation theory model, which realizes the on-demand dynamic adjustment of the network congestion time window. Finally, SUMO, MOVE, and NS2 tools are used to create a VANET-like environment to evaluate the performance of the proposed congestion control strategy. Experimental results show that the proposed method is superior to other three methods in the four indicators of average delay time, average transmission rate, number of retransmissions, and packet loss rate compared with other advanced methods.

ICMA: An Efficient Integrated Congestion Control Approach

Background: With the growing popularity of various group communication applications, such as file transfer, multimedia events, distance learning, email distribution, multiparty video conferencing, and teleconferencing, multicasting seems to be a useful tool for efficient multipoint data distribution. An efficient communication technique depends on various parameters like processing speed, buffer storage, and amount of data flow between the nodes. If data exceeds beyond the capacity of a link or node, then it introduces congestion in the network. A series of multicast congestion control algorithms have been developed, but due to the heterogeneous network environment, these approaches do not respond nor reduce congestion quickly whenever network behavior changes. Objective: Multicasting is a robust and efficient one-to-many (1: M) group transmission (communication) technique to reduced communication cost, bandwidth consumption, processing time, and delays with similar reliability (dependability) as of regular unicast. This patent presents a novel and comprehensive congestion control method known as an integrated multicast congestion control approach (ICMA) to reduce packet loss. Methods: The proposed mechanism is based on a leave-join flow control mechanism along with a Proportional Integrated and Derivate (PID) controller to reduce packet loss, depending on the congestion status. In the proposed approach, the Proportional integrated and derivate controller computes expected incoming rate at each router and feedback this rate to upstream routers of the multicast network to stabilize their local buffer occupancy. Results: Simulation results using NS-2 exhibit the immense performance of the proposed approach in terms of delay, throughput, bandwidth utilization, and packet loss than other existing methods. Conclusion: The proposed congestion control scheme provides better bandwidth utilization and throughput than other existing approaches. Moreover, we have discussed existing congestion control schemes with their research gaps. In the future, we are planning to explore the fairness and quality of service issues in multicast communication.

Q‐learning‐based congestion control strategy for information‐centric networking

The traditional congestion control strategies cannot be used for Information‐Centric Networking (ICN), and unfortunately the current proposals on ICN congestion control have also some limitations, such as high load, inaccurate information acquisition and inefficient cooperation. In this paper, we exploit the Q‐learning‐based reinforcement learning method to further address the ICN congestion control issue. Meanwhile, the Q‐learning is used to learn the network state, at the same time, a value which depends on the transmission delay is generated. In particular, the generated value is considered as a key factor and employed to do the decision forwarding for each routing node, of which the process is the hop‐by‐hop mode. Based on a simulated multipath topology, we compare the proposed congestion control strategy with two latest benchmarks by testing packet loss ratio and throughput, and the results show that it is more efficient.

A Weighted Assignment Approach Using Recommendation Routing for Congestion Control in Wireless Sensor Networks-WEAR

Background: Congestion can happen in WSNs while congregating the information and sending it towards the sink. It leads to increased packet delay, indiscriminate packet loss, severe fidelity degradation and wasted node energy. Objective: The aim of the proposed work is to suppress congestion using a dynamic weight assignment scheme, where each sensor node transmits the data in accordance to the weight assigned to it, thus ensuring priority fairness and minimizing packet loss. The proposed congestion control technique shall boost the overall performance of the system by supporting assured delivery of high importance events to sinks. Methods: Weights are assigned to each record based on two things, the delay in sending and receiving, and the change in value of the variables passed by the nodes. If the difference in timestamp is below a threshold (alpha) and the difference in the values is above a threshold (Beta), it gets a weight of W1, which is like a high priority data record. Similarly, if the difference in timestamp is above a threshold (gamma) and the values passed by the nodes have changed a negligible amount, that record is assigned a weight of W3, which is the least priority. Results: From the analysis, it is inferred that proposed method had a higher throughput and the throughput was equally distributed among all the participating nodes. Conclusion: The proposed method endeavours to avoid congestion by implementing an effective queuing mechanism to reinforce definitive wireless communication.

RETRACTED ARTICLE: Congestion control load balancing adaptive routing protocols for random waypoint model in mobile ad-hoc networks

Congestion is the main issue for routing in the mobile system which overall degrading performance in the network due to inadequate accessibility of resources of the wireless network of the nature, node mobility and dynamic topology of the wireless network. Congestion causes packet loss, time and energy wastage by connection failure and by mobile node failure. Congestion could be recognized as a base of deployment, link capacity node and routing routes of the network. The congestion detected on node-link by a source node along with the node of the path, which distributes traffic flow over the alternative node paths by considering routing path availability of the network. This paper has proposed a congestion control load balancing adaptive routing protocol (CCLBARP) algorithms on the direction to reduce delay, system routing overhead, congestion and enhances the life of network in MANET. The proposed congestion control technique is compared with original existing routing protocols such as DYMO and DSR of MANET in terms of throughput, end-to-end delay, packet drops average jitter, packet delivery ratio and normalized routing overhead of the network and this CCLBARP get outperformed the DSR and DYMO.

A fair and distributed congestion control mechanism for smart grid neighborhood area networks

The need for significant improvements in the management and efficient use of electrical energy has led to the evolution from the traditional electrical infrastructures towards modern Smart Grid networks. Taking into account the critical importance of this type of networks, multiple research groups focus their work on issues related to the generation, transport and consumption of electrical energy. One of the key research points is the data communication network associated with the electricity transport infrastructure, and specifically the network that interconnects the devices in consumers’ homes, the so-called Neighborhood Area Networks (NANs). In this paper, a new fair and distributed congestion control mechanism for NANs is proposed, implemented and evaluated. The main goal of this mechanism is to provide fairness in the access to the network, thus avoiding that some network nodes monopolize the use of the channels due to their higher traffic generation rate, or to their geographical position. Besides, different priorities have been considered for the traffic flows transmitted by different applications. The goal here is to provide the needed Quality of Service (QoS) to all traffic flows, especially when the traffic load is high. The proposal is evaluated in the context of a wireless ad hoc network composed by a set of smart grid meter devices. Applying our proposed congestion control mechanism leads to performance improvements in terms of packet delivery ratio, network throughput fairness between different traffic sources, packet network transit time and QoS provision.

Congestion Free Routing Mechanism for IoT-Enabled Wireless Sensor Networks for Smart Healthcare Applications

Recently, the Internet of Things (IoT) topology has used to collect physical, physiological, vital signs of patients in consumer-centric e-health or consumer' wellness care services. In such healthcare systems, varieties of medical sensors are attached to the patients to collect vital signs from those who are under observation. The data gathering process in IoT-enabled Wireless Sensor Network (WSN) suffers from the congestion problem. The effect of this translates on missing packets, a decrease of reliability and throughput degradation in IoT-enabled WSN. This article proposes a distributed congestion control algorithm for IoT-enabled WSNs to effectively resolve the congestion for healthcare applications. The proposed scheme alleviates congestion by a priority-based data routing strategy. Furthermore, this article presents a priority queue based scheduling scheme for better reliability. We analyze the properties of the proposed congestion control mechanism mathematically and validate its performance through extensive simulation and real-life experiments. The application of this work can be used to an early warning system in detecting abnormal heart rate, blood pressure, ECG, EMG in the hospital/home care environment to the state-of-art diagnosis.

An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System

The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms.

Analysis of Traffic Impact on Proposed Congestion Control Scheme in AODV

Congestion is a noteworthy problem in Mobile Ad hoc Network, which occurs when number of users increases and a node carries data more than its capacity. In this paper, a congestion control mechanism is suggested for the performance improvement of basic routing protocol AODV. The congestion status of a node is valuated based on parameters node queue length, channel utilization and residual node energy using fuzzy logic control. To strengthen the congestion control, an adaptive network coding mechanism is employed in case of high congestion to minimize transmissions amongst nodes. Recovery of data flow with neighbouring non-congested node is performed in case of medium congestion to avoid condition of high congestion and to reduce recovery time. Modifications are made to the AODV protocol in order to develop congestion control scheme without any additional overhead. The proposed methods are tested for different terrain areas with change in number of nodes and traffic load. The two variants of proposed protocol are simulated on Network simulator and impact of traffic on proposed schemes is investigated. The results are compared with basic AODV protocol. From simulation results, it is evident that adaptive network coding and congested link repairing method has improved scalability performance of routing protocol.

Congestion Control using Fuzzy based Adaptive TCP Window Adjustment

Objectives: To design an adaptive window adjustment mechanism for Transmission Control Protocol (TCP) in Mobile Ad hoc NETworks (MANET). Methods/Statistical Analysis: A congestion control using Fuzzy based adaptive TCP window adjustment technique is proposed. In this technique, the bottleneck bandwidth and total round-trip delay metrics are considered as input for the fuzzy logic decision model. The corresponding congestion window adjustment size is returned as the output. The proposed Fuzzy TCP-WA technique is simulated using NS2 and compared with FL-TCP. The proposed TCP-WA technique can be applied in interactive real-time applications like live chat session and File Transfer Protocol (FTP). Simulation results using NS2 suggest that the proposed congestion control technique Findings: educes the packet drop and delay and increases the throughput. Keywords: Bandwidth and Delay Product, Bottleneck Bandwidth, Congestion Control, Congestion Window, Fuzzy Logic, Roundtrip Delay, Transmission Control Protocol (TCP)

Congestion Window Scaling Method to Optimize Delay in TCP/IP

TCP (Transmission Control Protocol) is a protocol of layer four (Transport Layer), and IP (Internet Protocol) is in the Network Layer (Layer 3) of the OSI Model. The TCP protocol is the most used of the network applications on the Internet. TCP performance is strongly influenced by its congestion control algorithms that limit the amount of transmitted traffic based on the estimated network capacity and utilization. TCP is a connection-oriented protocol which guarantees the sent packets to the destination in accurate order whereas UDP is an unreliable/connectionless protocol which does not guarantee that the sent messages would reach the destination. A TCP connection is used to control packet sizes, the rate of data exchange, flow control and network congestion. Unfortunately, congestion/network overload happens in the TCP connection which affects the rate of data flow, loss in packets transmitted, packet transmission delay and delayed acknowledgment. In this paper, a method to scale the congestion window on the sender and receiver to optimize the delay of transmission in TCP in order to reduce the congestion rate is proposed. The proposed congestion control algorithm enables a sender to optimize and initialize the congestion window before transmitting packets. The proposed method is evaluated by simulating the transmission in Network Simulation 3 (NS3).

Congestion Control and Traffic Scheduling for Collaborative Crowdsourcing in SDN Enabled Mobile Wireless Networks

Currently, a number of crowdsourcing‐based mobile applications have been implemented in mobile networks and Internet of Things (IoT), targeted at real‐time services and recommendation. The frequent information exchanges and data transmissions in collaborative crowdsourcing are heavily injected into the current communication networks, which poses great challenges for Mobile Wireless Networks (MWN). This paper focuses on the traffic scheduling and load balancing problem in software‐defined MWN and designs a hybrid routing forwarding scheme as well as a congestion control algorithm to achieve the feasible solution. The traffic scheduling algorithm first sorts the tasks in an ascending order depending on the amount of tasks and then solves it using a greedy scheme. In the proposed congestion control scheme, the traffic assignment is first transformed into a multiknapsack problem, and then the Artificial Fish Swarm Algorithm (AFSA) is utilized to solve this problem. Numerical results on practical network topology reveal that, compared with the traditional schemes, the proposed congestion control and traffic scheduling schemes can achieve load balancing, reduce the probability of network congestion, and improve the network throughput.

A Game Theoretic Framework for Congestion Control in Named Data Networking

As an essential building block of the Named Data Networking (NDN) architecture, congestion mechanism has been the focus of much attention. The unique features of NDN architecture bring forward distinct requirements for congestion control from its Internet Protocol (IP) counterpart. In this paper, we present a game theoretic framework for flow rate control in NDN based on the concept of Nash bargaining solution from cooperative game theory, and propose a distributed, flow-aware and hop-by-hop congestion control mechanism on a solid analytical basis. In addition, we developed a possible implementation in ndnSIM and performed various experiments with different settings to study the behavior of our scheme. We have seen that the proposed congestion control mechanism performs as designed, and significantly enhances the network performance. DOI: http://dx.doi.org/10.5755/j01.itc.46.4.15680

Congestion Control for Web Real-Time Communication

Applications requiring real-time communication (RTC) between Internet peers are ever increasing. RTC requires not only congestion control but also minimization of queuing delays to provide interactivity. It is known that the well-established transmission control protocol congestion control is not suitable for RTC due to its retransmissions and in-order delivery mechanisms, which induce significant latency. In this paper, we propose a novel congestion control algorithm for RTC, which is based on the main idea of estimating—using a Kalman Filter—the end-to-end one-way delay variation which is experienced by packets traveling from a sender to a destination. This estimate is compared with a dynamic threshold and drives the dynamics of a controller located at the receiver, which aims at maintaining queuing delays low, while a loss-based controller located at the sender acts when losses are detected. The proposed congestion control algorithm has been adopted by Google Chrome. Extensive experimental evaluations have shown that the algorithm contains queuing delays while providing intra and inter protocol fairness along with full link utilization.

Energy‐aware congestion control scheme in opportunistic networks

In opportunistic networks, a congestion phenomenon may occur if the remaining buffer of a node cannot accept any arrival message. Energy is consumed by dropping the messages stored in the buffer to provide space for new messages. In order to reduce and balance the energy consumption of nodes during congestion in opportunistic networks, a novel congestion control scheme is proposed. Nodes dynamically update their residual energy level and query the residual energy level of their neighbors in current local environment and adjust their threshold ratio using the learning automata principle while moving around. Nodes will only receive messages whose remaining TTL (time to live) proportion of initial TTL is larger than the threshold ratio when congestion occurs. Simulation results show that the Epidemic routing protocol with the proposed congestion control scheme can efficiently reduce and balance energy consumption among nodes. Also, the routing performances such as delivery ratio, overhead ratio, and deliver latency are improved. Furthermore, the network lifetime is obviously improved since much more energy is left. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm

In an urban environment, intersections are critical locations in terms of road crashes and number of killed or injured people. Vehicular ad hoc networks (VANETs) can help reduce the traffic collisions at intersections by sending warning messages to the vehicles. However, the performance of VANETs should be enhanced to guarantee delivery of the messages, particularly safety messages to the destination. Data congestion control is an efficient way to decrease packet loss and delay and increase the reliability of VANETs. In this paper, a centralized and localized data congestion control strategy is proposed to control data congestion using roadside units (RSUs) at intersections. The proposed strategy consists of three units for detecting congestion, clustering messages, and controlling data congestion. In this strategy, the channel usage level is measured to detect data congestion in the channels. The messages are gathered, filtered, and then clustered by machine learning algorithms. $K$ - means algorithm clusters the messages based on message size, validity of messages, and type of messages. The data congestion control unit determines appropriate values of transmission range and rate, contention window size, and arbitration interframe spacing for each cluster. Finally, RSUs at the intersections send the determined communication parameters to the vehicles stopped before the red traffic lights to reduce communication collisions. Simulation results show that the proposed strategy significantly improves the delay, throughput, and packet loss ratio in comparison with other congestion control strategies using the proposed congestion control strategy.