Статті в журналах: "Congestion control algorithms"

1

Hasan, Husam H., and Zainab T. Alisa. "Effective IoT Congestion Control Algorithm." Future Internet 15, no. 4 (March 31, 2023): 136. http://dx.doi.org/10.3390/fi15040136.

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The Internet of Things (IoT) connects devices via the Internet. Network congestion is one of the key problems that has been identified by researchers in the IoT field. When there is a huge number of IoT devices connected to the internet, this creates network congestion. Transfer control protocol is a transport layer protocol that provides a reliable end-to-end connection between two devices. Many Congestion Control Algorithms have been proposed to solve network congestion. However, there is no perfect solution to this problem. This paper proposes an effective loss-based Congestion Control Algorithm to effectively adapt the congestion window in the IoT environment. It uses simple experiment scenarios to test the algorithm for wired and wireless channels and observes important performance metrics: link utilization, inter-protocol fairness, intra-protocol fairness and throughput. The results are impressive, and the proposed algorithm is shown to outperform other standard algorithms.

2

Tessler, Chen, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, and Shie Mannor. "Reinforcement Learning for Datacenter Congestion Control." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12615–21. http://dx.doi.org/10.1609/aaai.v36i11.21535.

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We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios. Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We overcome challenges such as partial-observability, non-stationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability. We show that these challenges prevent standard RL algorithms from operating within this domain. Our experiments, conducted on a realistic simulator that emulates communication networks' behavior, show that our method exhibits improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters. Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world.

3

LE, Tuan Anh, Choong Seon HONG, and Sungwon LEE. "Multipath Binomial Congestion Control Algorithms." IEICE Transactions on Communications E95.B, no. 6 (2012): 1934–43. http://dx.doi.org/10.1587/transcom.e95.b.1934.

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4

Tessler, Chen, Yuval Shpigelman, Gal Dalal, Amit Mandelbaum, Doron Haritan Kazakov, Benjamin Fuhrer, Gal Chechik, and Shie Mannor. "Reinforcement Learning for Datacenter Congestion Control." ACM SIGMETRICS Performance Evaluation Review 49, no. 2 (January 17, 2022): 43–46. http://dx.doi.org/10.1145/3512798.3512815.

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We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios. Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We overcome challenges such as partial-observability, nonstationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability. We show that this scheme outperforms alternative popular RL approaches, and generalizes to scenarios that were not seen during training. Our experiments, conducted on a realistic simulator that emulates communication networks' behavior, exhibit improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters. Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world.

5

Mahawish, Amar A., and Hassan J. Hassan. "Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (September 1, 2021): 1419. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1419-1431.

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The congestion on the internet is the main issue that affects the performance of transition data over the network. An algorithm for congestion control is required to keep any network efficient and reliable for transfer traffic data of the users. Many Algorithms had been suggested over the years to improve the control of congestion that occurs in the network such as drop tail packets. Recently there are many algorithms have been developed to overcome the drawback of the drop tail procedure. One of the important algorithms developed is active queue management (AQM) that provides efficient congestion control by reducing drop packets, this technique considered as a base for many other congestion control algorithms schema. It works at the network core (router) for controlling the drop and marking of packets in the router's buffer before the congestion inception. In this study, a comprehensive survey is done on the AQM Algorithm schemas that proposed and modification these algorithms to achieve the best performance, the classification of AQM algorithms based on queue length, queue delay, or both. The advantages and limitations of each algorithm have been discussed. Also, debate the intelligent techniques procedure with AQM algorithm to achieve optimization in performance of algorithm operation. Finally, the comparison has been discussed among algorithms to find the weakness and powerful of each one based on different metrics.

6

Mudassir, Mumajjed Ul, and M. Iram Baig. "MFVL HCCA: A Modified Fast-Vegas-LIA Hybrid Congestion Control Algorithm for MPTCP Traffic Flows in Multihomed Smart Gas IoT Networks." Electronics 10, no. 6 (March 18, 2021): 711. http://dx.doi.org/10.3390/electronics10060711.

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Multihomed smart gas meters are Internet of Things (IoT) devices that transmit information wirelessly to a cloud or remote database via multiple network paths. The information is utilized by the smart gas grid for accurate load forecasting and several other important tasks. With the rapid growth in such smart IoT networks and data rates, reliable transport layer protocols with efficient congestion control algorithms are required. The small Transmission Control Protocol/Internet Protocol (TCP/IP) stacks designed for IoT devices still lack efficient congestion control schemes. Multipath transmission control protocol (MPTCP) based congestion control algorithms are among the recent research topics. Many coupled and uncoupled congestion control algorithms have been proposed by researchers. The default congestion control algorithm for MPTCP is coupled congestion control by using the linked-increases algorithm (LIA). In battery powered smart meters, packet retransmissions consume extra power and low goodput results in poor system performance. In this study, we propose a modified Fast-Vegas-LIA hybrid congestion control algorithm (MFVL HCCA) for MPTCP by considering the requirements of a smart gas grid. Our novel algorithm operates in uncoupled congestion control mode as long as there is no shared bottleneck and switches to coupled congestion control mode otherwise. We have presented the details of our proposed model and compared the simulation results with the default coupled congestion control for MPTCP. Our proposed algorithm in uncoupled mode shows a decrease in packet loss up to 50% and increase in average goodput up to 30%.

7

Wang, Jiashuai, Xiaoping Yang, Ying Liu, and Zhihong Qian. "A Contention-Based Hop-By-Hop Bidirectional Congestion Control Algorithm for Ad-Hoc Networks." Sensors 19, no. 16 (August 9, 2019): 3484. http://dx.doi.org/10.3390/s19163484.

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Existing hop-by-hop congestion control algorithms are mainly divided into two categories: those improving the sending rate and those suppressing the receiving rate. However, these congestion control algorithms have problems with validity and limitations. It is likely that the network will be paralyzed due to the unreasonable method of mitigating congestion. In this paper, we present a contention-based hop-by-hop bidirectional congestion control algorithm (HBCC). This algorithm uses the congestion detection method with queue length as a parameter. By detecting the queue length of the current node and the next hop node, the congestion conditions can be divided into the following four categories: 0–0, 0–1, 1–0, 1–1 (0 means no congestion, 1 means congestion). When at least one of the two nodes is congested, the HBCC algorithm adaptively adjusts the contention window of the current node, which can change the priority of the current node to access the channel. In this way, the buffer queue length of the congested node is reduced. When the congestion condition is 1–1, the hop-by-hop priority congestion control (HPCC) method proposed in this paper is used. This algorithm adaptively changes the adjustment degree of the current node competition window and improves the priority of congestion processing of the next hop node. The NS2 simulation shows that by using the HBCC algorithm, when compared with distributed coordination function (DCF) without congestion control, the proposed unidirectional congestion control algorithms hop-by-hop receiving-based congestion control (HRCC) and hop-by-hop sending-based congestion control (HSCC), and the existing congestion control algorithm congestion alleviation—MAC (CA-MAC), the average saturation throughput increased by approximately 90%, 62%, 12%, and 62%, respectively, and the buffer overflow loss ratio reduced by approximately 80%, 79%, 44%, and 79%.

8

Liu, Lei, Feng Mei Zhang, Cun Wu Han, Song Bi, and De Hui Sun. "Adaptive Fault-Tolerant Congestion Control for Internet." Applied Mechanics and Materials 644-650 (September 2014): 2467–70. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2467.

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Existing Internet congestion control algorithms did not consider any faults. This paper presents a state feedback fault-tolerant congestion control algorithm which is based on adaptive and robust H∞ approaches. Simulation result shows that the proposed algorithm has good performance.

9

Utsumi, Satoshi. "Congestion Control Algorithms for the Internet – A Secondary Publication." Journal of Electronic Research and Application 8, no. 2 (March 29, 2024): 33–48. http://dx.doi.org/10.26689/jera.v8i2.6452.

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In the last five years, there has been a V-shaped recovery in the number of papers on congestion controlalgorithms on the Internet. In this paper, congestion problems on the Internet are discussed, such as congestion collapse and bufferbloat from the perspective of the necessity of congestion control algorithms. The typical congestion control algorithms are introduced, and the research areas and methods of congestion control algorithms are described. Recent research trends and future prospects of congestion control algorithms are also presented.

10

Han, Cun Wu, De Hui Sun, and Lei Liu. "Adaptive Congestion Control with Time-Varying Uncertainties." Applied Mechanics and Materials 556-562 (May 2014): 2285–88. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2285.

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Existing Internet congestion control algorithms did not consider time-varying uncertainties. This paper presents a new congestion control algorithm based on adaptive technique and robust H∞ approach, which can effectively compensate for the time-varying uncertainties. Simulation result shows that the proposed algorithm has good performance.

11

Zhang, Shu Guang, Qiao Yun Sun, Qing Hua Gao, Min Wang, and Yu Zhang. "Research on TCP Congestion Control Mechanisms." Advanced Materials Research 989-994 (July 2014): 4427–31. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4427.

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With the rapid development of information society, the number of information data transmitting in the Internet is growing quickly, so congestion control becomes very important and it is a hot research field on the Internet now. In this paper, the sliding window, reasons and TCP algorithms of congestion control such as slow start, congestion avoidance, fast retransmit and fast recovery are discussed. Different TCP versions about congestion control are studied and analyzed. As a result, improvements on the congestion control algorithm and the research is still an important topic in the study of Internet congestion control.

12

Suwannapong and Khunboa. "Congestion Control in CoAP Observe Group Communication." Sensors 19, no. 15 (August 5, 2019): 3433. http://dx.doi.org/10.3390/s19153433.

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The Constrained Application Protocol (CoAP) is a simple and lightweight machine-to-machine (M2M) protocol for constrained devices for use in lossy networks which offers a small memory capacity and limited processing. Designed and developed by the Internet Engineering Task Force (IETF), it functions as an application layer protocol and benefits from reliable delivery and simple congestion control. It is implemented for request/response message exchanges over the User Datagram Protocol (UDP) to support the Internet of Things (IoT). CoAP also provides a basic congestion control mechanism. In dealing with its own congestion, it relies on a fixed interval retransmission timeout (RTO) and binary exponential backoff (BEB). However, the default CoAP congestion control is considered to be unable to effectively perform group communication and observe resources, and it cannot handle rapid, frequent requests. This results in buffer overflow and packet loss. To overcome these problems, we proposed a new congestion control mechanism for CoAP Observe Group Communication, namely Congestion Control Random Early Detection (CoCo-RED), consisting of (1) determining and calculating an RTO timer, (2) a Revised Random Early Detection (RevRED) algorithm which has recently been developed and primarily based on the buffer management of TCP congestion control, and (3) a Fibonacci Pre-Increment Backoff (FPB) algorithm which waits for backoff time prior to retransmission. All the aforementioned algorithms were therefore implemented instead of the default CoAP mechanism. In this study, evaluations were carried out regarding the efficiency of the developed CoCo-RED using a Cooja simulator. The congestion control mechanism can quickly handle the changing behaviors of network communication, and thus it prevents the buffer overflow that leads to congestions. The results of our experiments indicate that CoCo-RED can control congestion more effectively than the default CoAP in every condition.

13

Liu, Chang Hua, and Cao Yuan. "Stability of Primal-Dual Algorithm with Communication Delay for Congestion Control." Advanced Materials Research 219-220 (March 2011): 513–17. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.513.

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Stability is a crucial issue for transport protocols. A lot of works study how to tune the parameters of congestion control algorithms to keep the stability of networks. This paper uses the idea of re-engineering to design an adaptive-stability congestion control algorithms base on the optimization theories. Furthermore, we present one congestion control algorithm according to different stable conditions. The effectiveness of these stability criteria is validated by simulation results．

14

Altman, E., K. E. Avrachenkov, and B. J. Prabhu. "Fairness in MIMD Congestion Control Algorithms." Telecommunication Systems 30, no. 4 (December 2005): 387–415. http://dx.doi.org/10.1007/s11235-005-5498-2.

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15

Utsumi, Satoshi. "Congestion Control Algorithms for the Internet." IEICE Communications Society Magazine 17, no. 1 (2023): 68–79. http://dx.doi.org/10.1587/bplus.17.68.

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16

Xiao, Fei. "Congestion and Computer Program Control Algorithm Strategy for Wireless Sensor Networks Based on Cloud Model." Wireless Communications and Mobile Computing 2022 (April 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/9160484.

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Cloud model and sensor network are the research hotspots in recent years. This paper proposes a congestion and rate control strategy for wireless sensor networks based on cloud model. It adjusts the input rate of nodes based on cloud model through node congestion detection. Aiming at the problem of network congestion control, two congestion adjustment algorithms based on red are improved. Congestion threshold and congestion degree are used as the basis of packet transmission rate adjustment to realize network support plug control. In this paper, the congestion control strategy NP starts to alleviate congestion at about 40 s and controls the packet loss rate at about 116 packets/s, which is 45.3% lower than the multipath congestion control strategy and is more stable. However, when β = 0 , the packet loss rate of the double congestion threshold algorithm is 20% lower than that of the single congestion threshold algorithm. The reason is that the double congestion threshold algorithm allows a stronger source rate control mechanism earlier than the single congestion threshold algorithm a large number of packets are lost in a point, but this also makes the network performance relatively low.

17

Fan, Xun Li, Fei Fei Du, and Zhen Hua Xie. "Input-Rate Based Adaptive Fuzzy Neuron PID Control for AQM." Advanced Materials Research 846-847 (November 2013): 3–8. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.3.

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Internet routers play an important role during network congestion. All the routers have buffers at input and output ports to hold the packets at congestion. Various congestion control algorithms have been proposed to control the congestion. Recently, some proportional-integral-derivative (PID) controller based algorithms have been proposed as Active Queue Management (AQM) schemes to address performance degradations of end-to-end TCP congestion control. However, most of the proposed PID-controllers for AQM are validated for their performance and stability via intuitive explanation and simulation studies instead of theoretic analysis and performance evaluation. But there are a few drawbacks of PID-controller based AQM algorithms leading to poor performance like causing data retention dropping and oscillation when the time delay is large, which means that the existing PID-controller can not meet the Quality of Service (QoS) requirements. To overcome the drawbacks of traditional PID, we analyze and enhance the PID-controller based AQM algorithm by regarding the TCP congestion control mechanism as an input-rate based Adaptive Fuzzy Neuron PID control algorithm (IRAFNPID) to avoid congestion in TCP/AQM networks. By means of simulations, we evaluate and compare the performance of traditional PID, single neural adaptive PID(SNAPID) and IRAFNPID, simulations with experiment data analysis and find that IRAFNPID has better convergence, stability, robustness, goodput and lower loss ratio.

18

Moosakhah, Fatemeh, and Amir Massoud Bidgoli. "Congestion Control in Computer Networks with a New Hybrid Intelligent Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 8 (August 23, 2014): 4688–706. http://dx.doi.org/10.24297/ijct.v13i8.7068.

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With invention of computer networks, transferring data from one computer to another became possible, but as the number of computers that transfer data to each other increased and common communication channel bandwidth among them in a network limited, has led to a phenomenon called congestion, so that some of data packets would be dropped and never arrive to destination. Different algorithms have been proposed for overcoming congestion. These are divided into two general groups: 1- flow based algorithms and 2- class based algorithms. In present study, using class based algorithm with optimization of its control by fuzzy logic and new Cuckoo algorithm, we increased the number of packets that reach to destination and reduced the number of dropped packets considerably during congestion. Simulation results indicate a great improvement of efficiency.

19

Jowkarishasaltaneh, Farinaz, and Jason But. "An Analysis of MPTCP Congestion Control." Telecom 3, no. 4 (October 19, 2022): 581–609. http://dx.doi.org/10.3390/telecom3040033.

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Many devices contain more than one network interface. There is scope for multi-path transfer to utilise these network interfaces simultaneously. Multi-path TCP (MPTCP) is designed to provide improved resilience and resource utilisation through multi-path transfer. One of the key goals of MPTCP is to preserve fair resource sharing with regular TCP at network bottlenecks. Although the coupled congestion control algorithms can achieve this goal by coupling subflow congestion windows, the algorithms always assume that the subflow paths will share a bottleneck. As a consequence, MPTCP is unable to maximise throughput over all available paths at a non-shared bottleneck. We present a survey about MPTCP and its coupled congestion control algorithms. We then show that MPTCP coupled congestion control algorithms perform poorly when paths are disjoint and/or do not have similar delay and/or bandwidth characteristics.

20

Pan, Wansu, Haibo Tan, Xiru Li, and Xiaofeng Li. "Improved RTT Fairness of BBR Congestion Control Algorithm Based on Adaptive Congestion Window." Electronics 10, no. 5 (March 6, 2021): 615. http://dx.doi.org/10.3390/electronics10050615.

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To alleviate the lower performance of Transmission Control Protocol (TCP) congestion control over complex network, especially the high latency and packet loss scenario, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm. In contrast with other TCP congestion control algorithms, BBR adjusted transfer data by maximizing delivery rate and minimizing delay. However, some evaluation experiments have shown that the persistent queues formation and retransmissions in the bottleneck can lead to serious fairness issues between BBR flows with different round-trip times (RTTs). They pointed out that small RTT differences cause unfairness in the throughput of BBR flows and flows with longer RTT can obtain higher bandwidth when competing with the shorter RTT flows. In order to solve this fairness problem, an adaptive congestion window of BBR is proposed, which adjusts the congestion window gain of each BBR flow in network load. The proposed algorithms alleviate the RTT fairness issue by controlling the upper limit of congestion window according to the delivery rate and queue status. In the Network Simulator 3 (NS3) simulation experiment, it shows that the adaptive congestion window of BBR (BBR-ACW) congestion control algorithm improves the fairness by more than 50% and reduces the queuing delay by 54%, compared with that of the original BBR in different buffer sizes.

21

Bazi, Kaoutar. "Comparative study of TCP congestion control algorithms." International Journal of Advanced Trends in Computer Science and Engineering 8, no. 6 (December 15, 2019): 3560–64. http://dx.doi.org/10.30534/ijatcse/2019/137862019.

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22

Lomuscio, Alessio, Ben Strulo, Nigel G. Walker, and Peng Wu. "Model Checking Optimisation Based Congestion Control Algorithms." Fundamenta Informaticae 102, no. 1 (2010): 77–96. http://dx.doi.org/10.3233/fi-2010-298.

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Song, Yeong-Jun, Geon-Hwan Kim, and You-Ze Cho. "Performance Evaluation between TCP Congestion Control Algorithms." Journal of Korean Institute of Communications and Information Sciences 44, no. 11 (November 30, 2019): 2102–13. http://dx.doi.org/10.7840/kics.2019.44.11.2102.

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24

Yan, Jiangyu, and Bing Qi. "CARA: A Congestion-Aware Routing Algorithm for Wireless Sensor Networks." Algorithms 14, no. 7 (June 30, 2021): 199. http://dx.doi.org/10.3390/a14070199.

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Congestion control is one of the key research topics in relation to the routing algorithms of wireless sensor networks (WSNs). In this paper, we propose a congestion-aware routing algorithm (CARA) for unlimited-lifetime wireless sensor networks by integrating the geographic distance and traffic load of sensor nodes. The algorithm takes alleviating congestion as the primary purpose and considers the traffic of the node itself and local network traffic. According to the geographic distance between nodes, CARA defines four decision parameters (node load factor, forward rate, cache remaining rate, and forward average cache remaining rate), selecting the best node as the next-hop through the multi-attribute decision-making method. Compared with the two existing algorithms for congestion control, our simulation results suggest that the CARA algorithm alleviates network congestion and meets reasonable network delay and energy consumption requirements.

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Elias, Shamsul Jamel, Shahirah Mohamed Hatim, Mohamad Yusof Darus, Shapina Abdullah, Jamaluddin Jasmis, R. Badlishah Ahmad, and Adam Wong Yoon Khang. "Congestion control in vehicular adhoc network: a survey." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (March 1, 2019): 1280. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp1280-1285.

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Vehicular adhoc network (VANET) has a significant potential in reducing traffic congestion to provide a stress-free and safer platform for road drivers to travel on the road. However, the current VANET is vulnerable to several challenges which need to be overcome. Congestion control is considered as one of the main challenges in VANET due to the high dynamic topology characteristic. Reliable congestion control (CC) are necessary to provide effectient dissemination of time-critical safety messages in VANET applications; safety and non-safety applications. In this paper, we present the overview on VANET, its application and challenges. We also discuss on the congestion control and provide a brief survey on the congestion control algorithms such as vehicular cloud computing, multiplicative rate decreasing algorithm, multi-objective Tabu search, D-FPAV algorithm and beaconing strategies which have been proposed in order to provide better solutions towards achieving a successful Smart Tranporation System.

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Yang, Dong, and Wei-Tek Tsai. "SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks." Sensors 24, no. 3 (January 24, 2024): 749. http://dx.doi.org/10.3390/s24030749.

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5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks.

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Song, Yeong-Jun, Geon-Hwan Kim, and You-Ze Cho. "BBR-CWS: Improving the Inter-Protocol Fairness of BBR." Electronics 9, no. 5 (May 22, 2020): 862. http://dx.doi.org/10.3390/electronics9050862.

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TCP congestion control adjusts the sending rate in order to protect Internet from the continuous traffic and ensure fair coexistence among multiple flows. Especially, loss-based congestion control algorithms were mainly used, which worked relatively well for past Internet with low bandwidth and small bottleneck buffer size. However, the modern Internet uses considerably more sophisticated network equipment and advanced transmission technologies, and loss-based congestion control can cause performance degradation due to excessive queueing delay and packet loss. Therefore, Google introduced a new congestion control in 2016, Bottleneck Bandwidth Round-trip propagation time (BBR). In contrast with traditional congestion control, BBR tries to operate at the Kleinrock’s optimal operating point, where delivery rate is maximized and latency is minimized. However, when BBR and loss-based congestion control algorithms coexist on the same bottleneck link, most of bottleneck bandwidth is occupied by flows that use a particular algorithm, and excessive packet retransmission can occur. Therefore, this paper proposes a BBR congestion window scaling (BBR-CWS) scheme to improve BBR’s inter-protocol fairness with a loss-based congestion control algorithm. Through Mininet experiment results, we confirmed that fairness between BBR-CWS and CUBIC improved up to 73% and has the value of 0.9 or higher in most bottleneck buffer environments. Moreover, the number of packet retransmissions was reduced by up to 96%, compared to the original BBR.

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Zhao, Zhicheng, and Weihua Cao. "Improved Bottleneck Bandwidth and Round-Trip Propagation Congestion Control Algorithm for Round-Trip Time Fairness." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 3 (May 20, 2023): 346–51. http://dx.doi.org/10.20965/jaciii.2023.p0346.

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Traditional congestion-control algorithms based on packet loss are more favorable for short round-trip time (RTT) flows. However, when the flow occupies the bottleneck bandwidth, the effect of bottleneck bandwidth and round-trip propagation time (BBR) congestion-control algorithm is on contrary. A short RTT flow may become “starved” after multiple iterations, which leads to a significant fairness problem in the BBR. This study analyzed the convergence using the BBR algorithm principle and the effect of RTT on fairness. Thereafter, we proposed an improved algorithm termed BBR-f, constructed an inverse proportional function of RTT, and added a gain function to the congestion window (CWND) gain. A simulation experiment using the ns-3 network simulator platform shows that the algorithm can optimize fairness and improve bottleneck-linked bandwidth utilization. Finally, the Jain fair index was used to evaluate its effectiveness, and a comparison experiment with traditional algorithms was performed to verify the efficiency of the proposed method.

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Yuan, Cao, and Ya Qin Li. "A New Delay-Based Congestion Control for Ad-Hoc Wireless Network." Applied Mechanics and Materials 160 (March 2012): 223–26. http://dx.doi.org/10.4028/www.scientific.net/amm.160.223.

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The traditional congestion control mechanism of transport control protocol (TCP) has been proved cannot satisfy the wireless scenario. The extensive research has been done to understand the drawback of TCP in wireless networks. In this paper, we proposed a new congestion control algorithms for mobile ad-hoc networks (MANET) at the TCP sender side which efficiently adapts to the maximum transmission rate of a mobile wireless link, the new algorithms follow the idea of FAST TCP which uses delay as congestion measure. However, FAST TCP has limitations when used over a dynamic mobile wireless link with a high frame error ratio (FER) and frequent delay changes due to the variable rate. The new algorithms overcome this shortcoming by improving the congestion signal choice. By using analysis, we proved that the new congestion control mechanism provides superior performance over mobile wireless network environments.

30

Zhang, Zhi Jing, and Zeng Xin Xie. "Study on the Adaptive Congestion Control Algorithms for Wireless Sensor Networks." Advanced Materials Research 989-994 (July 2014): 4493–96. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4493.

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As the study on and promotion of wireless sensor networks (WSN) are deepening, people have increasingly higher requirements for the transmission speed and transmission performance of wireless sensor networks. Network congestion is an important factor of the transmission performance of a network, so congestion control becomes a hot research topic in the studies on the improvement of WSN services. Based on the study on WSN congestion control mechanism, this paper proposes an adaptive congestion control algorithm and simulates the algorithm. The simulation result shows that this algorithm can ensure stable network performance.

31

Voice, T. "Stability of Multi-Path Dual Congestion Control Algorithms." IEEE/ACM Transactions on Networking 15, no. 6 (December 2007): 1231–39. http://dx.doi.org/10.1109/tnet.2007.899011.

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32

Kushwaha, Vandana, and Ratne shwer. "A Review of Router based Congestion Control Algorithms." International Journal of Computer Network and Information Security 6, no. 1 (November 7, 2013): 1–10. http://dx.doi.org/10.5815/ijcnis.2014.01.01.

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33

Zhang, Xuan, and Antonis Papachristodoulou. "Improving the Performance of Network Congestion Control Algorithms." IEEE Transactions on Automatic Control 60, no. 2 (February 2015): 522–27. http://dx.doi.org/10.1109/tac.2014.2336338.

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34

Bansal, Deepak, Hari Balakrishnan, Sally Floyd, and Scott Shenker. "Dynamic behavior of slowly-responsive congestion control algorithms." ACM SIGCOMM Computer Communication Review 31, no. 4 (October 2001): 263–74. http://dx.doi.org/10.1145/964723.383080.

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35

Zheng, Guorong, Yuke Liu, Yazhou Fu, Yingjie Zhao, and Zundong Zhang. "Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG." Sensors 23, no. 18 (September 19, 2023): 7975. http://dx.doi.org/10.3390/s23187975.

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As urban areas continue to expand, traffic congestion has emerged as a significant challenge impacting urban governance and economic development. Frequent regional traffic congestion has become a primary factor hindering urban economic growth and social activities, necessitating improved regional traffic management. Addressing regional traffic optimization and control methods based on the characteristics of regional congestion has become a crucial and complex issue in the field of traffic management and control research. This paper focuses on the macroscopic fundamental diagram (MFD) and aims to tackle the control problem without relying on traffic determination information. To address this, we introduce the Q-learning (QL) algorithm in reinforcement learning and the Deep Deterministic Policy Gradient (DDPG) algorithm in deep reinforcement learning. Subsequently, we propose the MFD-QL perimeter control model and the MFD-DDPG perimeter control model. We conduct numerical analysis and simulation experiments to verify the effectiveness of the MFD-QL and MFD-DDPG algorithms. The experimental results show that the algorithms converge rapidly to a stable state and achieve superior control effects in optimizing regional perimeter control.

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Lai, Cheng Yu, and Xiao Guang Fu. "Research of Hybrid Congestion Control Mechanism." Advanced Materials Research 426 (January 2012): 275–78. http://dx.doi.org/10.4028/www.scientific.net/amr.426.275.

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In wireless sensor networks, congestion causes overall channel quality to degrade and loss rates to raise, leads to buffer drops and increased delays, and tends to be grossly unfair toward nodes whose data has to traverse a larger number of radio hops. Hybrid congestion control mechanisms relieve the congestion by creating the new path; when establishment of a new path is failed, fairness aggregate mechanisms limits forward rate, ensures that each source node sends data fairly. Based on energy-saving, algorithms for mild congestion have been improved.

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Luo, Jing, Xiaoxu Xiao, and Rongxia Wang. "Artificial Intelligence Based Sensor Network Congestion Fuzzy Control Algorithm." Journal of Physics: Conference Series 2074, no. 1 (November 1, 2021): 012030. http://dx.doi.org/10.1088/1742-6596/2074/1/012030.

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Abstract The topology control of sensor sensor network was studied based on fuzzy control algorithm. Aiming at the dynamic changes of the topology of large-scale and heterogeneous artificial intelligence sensor networks and the incomplete information between nodes, a smart network-based congestion control algorithm for sensor networks was proposed and the performance of fuzzy control algorithms was analyzed. Based on this, a fuzzy control algorithm was designed. The algorithm fully considered the residual energy of nodes and the distribution of nodes in the network. Therefore, the reasonable election of the cluster head can be realized through the game between nodes, which effectively avoided energy holes, made the network energy consumption more uniform, prolonged the network life cycle, and optimized the network topology.

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Parsa, Ali, Neda Moghim, and Sasan Haghani. "Joint congestion and contention avoidance in a scalable QoS-aware opportunistic routing in wireless ad-hoc networks." PLOS ONE 18, no. 8 (August 1, 2023): e0288955. http://dx.doi.org/10.1371/journal.pone.0288955.

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Opportunistic routing (OR) can greatly increase transmission reliability and network throughput in wireless ad-hoc networks by taking advantage of the broadcast nature of the wireless medium. However, network congestion is a barrier in the way of OR’s performance improvement, and network congestion control is a challenge in OR algorithms, because only the pure physical channel conditions of the links are considered in forwarding decisions. This paper proposes a new method to control network congestion in OR, considering three types of parameters, namely, the backlogged traffic, the traffic flows’ Quality of Service (QoS) level, and the channel occupancy rate. Simulation results show that the proposed algorithm outperforms the state-of-the-art algorithms in the context of OR congestion control in terms of average throughput, end-to-end delay, and Packet Delivery Ratio (PDR). Due to the higher PDR at different traffic loads and different node densities, it can be concluded that the proposed algorithm also improves network scalability, which is very desirable given the recent changes in wireless networks.

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Ma, Jian Hong, and Li Xia Ji. "Shrew Attack on Internet Congestion Control Protocol in Control Engineering." Advanced Materials Research 648 (January 2013): 277–80. http://dx.doi.org/10.4028/www.scientific.net/amr.648.277.

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Low-rate Denial of Service(LDoS) attacks with their hidden,high efficiency features can significantly degrade service performance of large number of connection-oriented services,or even worse,thoroughly deny the services.Shrew attack is a typical LDoS attack.Firstly we studied the basic mechanism of the attack and congestion control.The source of adaptive congestion control mechanism in the security vulnerability was revealed according to the different levels of the intrinsic link between Internet congestion control at TCP layer and IP layer.Secondly,using the Network simulator NS2 software package,we set up attack model to simulate a large number of attack experiments with various congestion control mechanism and algorithms.Finally we draw the conclusions that continuous Shrew attack makes services nearly crash,while congestion control algorithms taking into account of fairness,such as Stochastic Fairness Queuing (SFQ) and Deficit Round Robin (DRR),can effectively suppress such kind of attack.

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Liu, Qiao, Zheng Kun Yang, and Gui Hua Duan. "Improved Congestion Control Algorithm in Wide Bandwidth and Long Delay Network." Applied Mechanics and Materials 462-463 (November 2013): 997–1000. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.997.

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With the idea of binary search increasing mechanism of BIC algorithm, an improved BIC (IBIC) algorithm is proposed by adjusting the congestion window with information of packet-loss and data delay, the flow chart and some important parameters are presented, the IBIC algorithm is carried out in Linux kernel. Moreover, the Linux-based network testing platform is constructed by utilizing squid to transmit data from server to clients in gateway, and using WANem to simulate the situation of bandwidth, data dropout and delay. By testing and comparing the congestion control capability of IBIC with other main algorithms, the IBIC algorithm is proved to increase effectively the network communication efficiency.

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Piotrowska, Agnieszka. "On Cross-Layer Interactions for Congestion Control in the Internet." Applied Sciences 11, no. 17 (August 25, 2021): 7808. http://dx.doi.org/10.3390/app11177808.

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Two key mechanisms of the Internet are congestion control in the Transmission Control Protocol (TCP) and Active Queue Management (AQM) in routers. The former divides the bandwidth between flows and prevents the Internet from congestion collapse. Simultaneously, the latter informs hosts of the forthcoming congestion by preventive dropping of packets in network nodes. Although these two key mechanisms may severely interact with each other, they are often being researched independently, in parallel. This has led to the development of a few new congestion controls and AQM algorithms known for excellent performance under the assumption that the counterpart remains unaltered. It is unclear, however, how these new solutions in both areas interact with each other. The purpose of this paper is to fill this gap. Namely, in an extensive set of simulations, the impact of interactions between the state-of-the-art congestion control and AQM algorithms on the TCP connection performance is studied. As a result, recommendations for using some particular TCP-AQM pairs, which are observed to perform especially well, are formulated.

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Ouakasse, Fathia, and Said Rakrak. "An Improved Adaptive CoAP Congestion Control Algorithm." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 03 (February 14, 2019): 96. http://dx.doi.org/10.3991/ijoe.v15i03.9122.

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<p class="0abstract"><span lang="EN-US">The Constrained Application Protocol (CoAP) is one of the most emerging messaging protocols that have successfully fulfilled the need of the lightweight feature required to handle communication between constrained devices in IoT environment. However, these devices are generating a huge amount of messages and notifications which cause the network congestion. Then, the challenge addressed in this paper; consists of designing a suitable congestion control mechanism for CoAP that ensures a safe network operation while keeping the use of network resources efficient. To do so, this paper presents an improved congestion control algorithm for the estimation of a Retransmission Time Out (RTO) value to use in each transaction based on the packet loss ratio and the Round-Trip Time RTT of the previous transmission. A comprehensive analysis and evaluation of simulated results show that the proposed mechanism can appropriately achieve higher performance compared to the basic CoAP congestion control and alternative algorithms based on TCP.</span></p>

43

Chaturvedi, Rajnish Kumar, Satish Chand, and Manoj Kumar Tyagi. "An Enhanced Multipath TCP." International Journal of E-Adoption 14, no. 3 (August 25, 2022): 1–16. http://dx.doi.org/10.4018/ijea.309998.

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Since 2020, the coronavirus disease 2019 (COVID-19) pandemic and lockdown policy have changed our life drastically. Due to this, companies, offices, schools, etc. are running in online mode. Therefore, the demand for internet bandwidth is very popular nowadays. Due to an increase in demand for internet bandwidth and the availability of multihomed devices, multipath TCP (MPTCP), and e-adoption of emerging technology enhanced bandwidth by utilizing the bandwidth of all available networks simultaneously. This paper analyzes the working principle of the MPTCP so that each internet device can easily use MPTCP to achieve high bandwidth. Load distribution and its performance mainly depend on the congestion control algorithm and the packet scheduler. MPTCP has various existing packet scheduling algorithms and congestion control algorithms. Here, the authors compare the performance of MPTCP with each congestion control algorithm and packet scheduler one by one and try to find out the best-performing MPTCP. This paper also shows that the MPTCP helps COVID-19 patients in various aspects.

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Pieska, Marcus, Andreas Kassler, Anna Brunstrom, Veselin Rakocevic, and Markus Amend. "Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling." Future Internet 16, no. 7 (June 28, 2024): 233. http://dx.doi.org/10.3390/fi16070233.

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Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm.

45

Fang, Juan, Di Zhang, and Xiaqing Li. "ParRouting: An Efficient Area Partition-Based Congestion-Aware Routing Algorithm for NoCs." Micromachines 11, no. 12 (November 25, 2020): 1034. http://dx.doi.org/10.3390/mi11121034.

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Routing algorithms is a key factor that determines the performance of NoC (Networks-on-Chip) systems. Regional congestion awareness routing algorithms have shown great potential in improving the performance of NoC. However, it incurs a significant queuing latency when practitioners use existing regional congestion awareness routing algorithms to make routing decisions, thus degrading the performance of NoC. In this paper, we propose an efficient area partition-based congestion-aware routing algorithm, ParRouting, which aims at increasing the throughput and reducing the latency for NoC systems. First, ParRouting partitions the network into two areas (i.e., edge area and central area.) based on node priorities. Then, for the edge area, ParRouting selects the output node based on different priorities for higher throughput; for the central area, ParRouting selects the node in the low congestion direction as the output node for lower queuing latency. Our experimental results indicate that ParRouting achieves a 53.4% reduction in packet average latency over SPLASH -2 ocean application and improves the saturated throughput by up to 38.81% over a synthetic traffic pattern for an NoC system, compared to existing routing algorithms.

46

Ren, Bin, Haocheng Luo, and Chunhong He. "A Review of Urban Real-time Traffic Signal Control." Advances in Engineering Technology Research 6, no. 1 (July 19, 2023): 498. http://dx.doi.org/10.56028/aetr.6.1.498.2023.

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Urban traffic congestion was a challenging problem. In recent years, researchers in the fields of transportation and computer science devoted considerable attention to studying real-time traffic signal control (RTSC) to alleviate urban traffic congestion. This paper reviewed various methods for RTSC, categorizing them based on the algorithms. Additionally, simulators were summarized to validate algorithm performances, and the analysis further explored the interrelationships between different traffic entities, optimization objectives, and network types. Finally, potential avenues for future research in traffic signal control (TSC) were discussed, with the aim of providing valuable references to researchers working on intelligent transportation systems (ITS) for practical implementations.

47

Jing, Qi, and Ming Ming Gu. "Active Queue Management Based on State Variable Feedback Control." Applied Mechanics and Materials 397-400 (September 2013): 2077–80. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2077.

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A new AQM algorithm based on state variable feedback control (SPC) is proposed. The future dynamic queue length in data buffer predicted by state space model is successfully introduced into feedback data's advanced prediction to compensate feedback delay. Finally, the control requirement of congestion is converted to optimal control objective function, and drop probability is obtained by solving the optimal problem. The simulation results show that the queue length with SPAQM algorithm reaches the desired value with minimal tracking error and lower drop probability. SPAQM algorithm has better performance than PID algorithms and RED algorithms in terms of disturbance rejection, stability, and robustness.

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Ishihara, Susumu, Kaito Furukawa, and Haruka Kikuchi. "Congestion Control Algorithms for Collective Perception in Vehicular Networks." Journal of Information Processing 30 (2022): 22–29. http://dx.doi.org/10.2197/ipsjjip.30.22.

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49

Raina, G. "Local bifurcation analysis of some dual congestion control algorithms." IEEE Transactions on Automatic Control 50, no. 8 (August 2005): 1135–46. http://dx.doi.org/10.1109/tac.2005.852566.

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50

Patel, Sanjeev, and Kritika Rani. "Comparative performance analysis of TCP-based congestion control algorithms." International Journal of Communication Networks and Distributed Systems 17, no. 1 (2016): 61. http://dx.doi.org/10.1504/ijcnds.2016.077939.

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