Congestion Control Algorithm Research Articles

UNDERSTANDING THE TRADE-OFF IN TCP'S CUBIC WINDOWS CONGESTION: A COMPREHENSIVE ANALYSIS

This study investigates the difficulties of the CUBIC TCP algorithm, focusing on the trade-off between efficiency and fairness in window congestion control. The CUBIC approach, which was devised to optimize high-bandwidth and long-distance networks, is currently widely used as the default TCP congestion control algorithm in Linux and other systems. Our work investigates the dynamics of CUBIC's window expansion, specifically the interaction between the cubic function and the most recent congestion event. We look at how this interplay impacts the distribution of window widths, throughput, round-trip time (RTT), loss rate, and queue size under different network configurations. Keywords: TCP CUBIC, Window Congestion, Efficiency-Fairness Trade-Off, TCP’s Cubic Function.

Improvement of Student Interaction Analysis in Online Education Platforms Through Interactive Mobile Technology and Machine Learning Integration

The emergence of online education platforms, driven by interactive mobile technology, has significantly reshaped traditional educational paradigms and underscored the critical need for advanced analysis and improvement of student interactions. Effective analysis of student interaction is crucial for enhancing teaching quality and optimizing the learning experience in these digitally enriched environments. Traditional analysis frameworks often face challenges such as inaccuracies in anomaly detection and inefficiencies in data handling, particularly when handling extensive datasets typical of online platforms. This study introduces a novel approach to enhancing student interaction analysis systems by leveraging the synergy between machine learning and advanced interactive mobile technologies. Initially, the study proposes an advanced anomaly detection method tailored for identifying irregular student interactions. This method utilizes a blend of machine learning algorithms and the real-time data processing capabilities of mobile technology. Furthermore, to address the complexities of data transmission in mobile-based online education ecosystems, a state-ofthe- art congestion control algorithm has been developed. This algorithm optimizes data flow, significantly enhancing transmission stability and efficiency. The integration of interactive mobile technology with machine learning offers a robust and dynamic framework for analyzing student interactions, thereby facilitating a more engaging and effective online educational experience. This research contributes to the advancement of online education quality and efficiency by emphasizing the role of interactive mobile technology in shaping future learning environments.

Performance Evaluation of Congestion Control Algorithm in TCP/IP Differentiated Services During COVID-19 Pandemic Due to Heavy Surge in Real-Time Traffic

Numerous nations implemented countrywide lockdowns because of the COVID-19 epidemic, which forced almost all peoples to remain at home. The imposition of confinement restrictions led to an increase in residential users' Internet traffic demands, particularly for distant work, entertainment, commerce, and education. This resulted in changes to the traffic patterns within the Internet core. In this research, we investigate the impact of the countrywide lockdowns on internet traffic based on dscp values by collecting data from class-A, class-B and Class-C ISP.(Three Class-A ISP, Three Class-B ISP and Three Class-C ISP) which leads to heavy congestion in the network and finally the performance valuation is carried out. The overall rise is exponential in nature, we notice that the traffic volume climbed by 15-30% almost in a quarter, which is a significant gain in such a short amount of time. Direct examination of the traffic sources reveals that, although hypergiants continue to account for a sizeable portion of traffic, there has been a greater increase in non-hypergiant traffic as well as in traffic from home-use applications like Web conferencing, VPNs, and gaming. Academic networks show significant overall declines whereas many networks, particularly those serving residential users, pharmaceutical companies, online interaction platform increases in traffic demands. However, we can see significant gains in these networks when we look at applications related to lecturing and remote working. The changes in the nature of use by customers show that there is a sharp difference in application pertaining to usage. Previous to this period, most usage is on YouTube, Facebook and google search. However, after this period most traffic diverted to online platforms such as zoom, google meet, Teams, and others as well as OTT platform comes into the picture. Over the past two decades, there has been a notable shift in user profiles. In the early 2010s, we saw most traffic was inclined on stored video and audio data in continuation with conventional traffic [23, 49, 66], which was followed by content delivery and streaming applications [7, 24, 35, 37, 52], and mobile applications [32, 67] in the 2010s. Even though user profile modifications are a moving target, they usually take years to complete. So, it was possible to stay current, for example, by measurements. The impact of this pandemic created chaotic conditions as most traffic migrated to real time surge data. Thus, the congestion management becomes more complicated as of previous challenges. The ISP forced to provide more bandwidth to real-time traffic which leads to overprovisioning. Thus, an efficient algorithm is required for managing such surge traffic which may especially meet real time traffic as required by most customers. This paper thus focuses on impact of after corona-19 pandemic effect, the shift of traffic by various users and applications the behavior of some existing algorithms in terms of congestion management and finally suggested a balanced method for managing congestion in such heavy surge traffic.

Congestion Control Algorithms for the Internet – A Secondary Publication

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.

A receiver-driven transport protocol using differentiated algorithms for differential congestion in datacenters

With the continuous development of applications, data centers run application traffic from various service providers, and the requirements for data center networks are also increasing. Congestion control has always been a hot topic of discussion. Prevalent sender-driven transport protocols require adjusting their sending rate after network congestion occurs, and the long feedback delay makes them susceptible to a buffer overflow. Therefore, receiver-driven transport protocols are proposed, and the receiver adopts credits to allocate downlink bandwidth to overcome network congestion. This type of scheme solves last-hop congestion and reduces congestion feedback latency. However, the existing receiver-driven transport protocol is insensitive to congestion location and thus reacts inaccurately to congestion. To address this problem, this paper proposes a congestion control algorithm with congestion location awareness. In this scheme, 1) we first design an ECN-based congestion detection method to achieve lightweight congestion types awareness, i.e., instantaneous congestion and continuous congestion. 2) Based on this, we further design a differentiated transmission control strategy. For instantaneous congestion, we adopt adaptive backoff and delay control algorithms. For continuous congestion, we employ a rate control algorithm to reduce in-network congestion. This does not require complex modifications to the switch. In our evaluation, the overall average flow completion time (FCT) of DCC is up to 21%, 89%, 1.5×, and 3.4× better than Homa, ExpressPass, Timely, and pHost, respectively.

A Temporal Deep Q Learning for Optimal Load Balancing in Software-Defined Networks.

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward-punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach-tDQN-effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss.

Enforcing Fairness in the Traffic Policer Among Heterogeneous Congestion Control Algorithms

Traffic policing is widely used by ISPs to limit their customers’ traffic rates. It has long been believed that a well-tuned traffic policer offers a satisfactory performance for TCP. However, we find this belief breaks with the emergence of new congestion control (CC) algorithms: flows using new CC algorithms can easily occupy the majority of bandwidth, starving traditional TCP flows. We confirm this problem with experiments and reveal its root cause as follows. Without a buffer in traffic policers, congestion only causes packet losses, while new CC algorithms are loss-resilient. When being policed, they will not reduce the sending rate until an unacceptable loss ratio for TCP is reached, resulting in low throughput for competing TCP flows. Simply adding a buffer to the traffic policer improves fairness but incurs high latency. To this end, we propose FairPolicer, which can achieve fair bandwidth allocation without sacrificing latency. FairPolicer regards a token as a basic unit of bandwidth and fairly allocates tokens to active flows in a round-robin manner. To avoid bandwidth waste when flows come and go, FairPolicer puts all available tokens in a global bucket and maintains the amount of residual bucket space rather than the number of available tokens. To scale to massive concurrent flows, FairPolicer uses a Count-Min Sketch structure to maintain per-flow data with a small memory footprint. Testbed experiments show that FairPolicer can allocate bandwidth in a max-min fair manner and achieve much lower latency than other kinds of rate limiters.

Enabling Delay-Guaranteed Congestion Control With One-Bit Feedback in Cellular Networks

Unexpected large packet delays are often observed in cellular networks due to huge network queuing caused by excessive traffic coming into the network. To deal with the large queue problem, many congestion control algorithms try to find out how much traffic the network can accommodate, either by measuring network performance or by directly providing explicit information. However, due to the nature of the control in which queue growth should be observed or the necessity to modify the overall network architecture, existing algorithms are experiencing difficulties in keeping queues within a strict bound. In this paper, we propose a novel congestion control algorithm based on simple feedback, ECLAT which can provide bounded queuing delay using only one-bit signaling already available in traditional network architecture. To do so, a base station or a router running ECLAT 1) calculates how many packets each flow should transmit and 2) analyzes when congestion feedback needs to be forwarded to adjust the flow’s packet transmission to the desired rate. Our extensive experiments in our testbed demonstrate that ECLAT achieves strict queuing delay bounds, even in the dynamic cellular network environment.

Improvement of Copa: Behaviors and Friendliness of Delay-Based Congestion Control Algorithm

Improvement of Copa: Behaviors and Friendliness of Delay-Based Congestion Control Algorithm

SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks

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.

Feedback-Based Control Loop Congestion Control Algorithm for Wireless Networks

Feedback-Based Control Loop Congestion Control Algorithm for Wireless Networks

Utilizing Machine Learning as a Prediction Scheme for Network Performance Metrics of Self-Clocked Congestion Control Algorithm

Congestion Control (CC) is a fundamental mechanism to achieve effective and equitable sharing of network facilities. As future networks evolve towards more complex paradigms, traditional CC methods are required to become more powerful and reliable. On the other hand, Machine Learning (ML) has become increasingly popular for solving challenging and sophisticated problems, and scientists have started to turn their interest from rule-based approaches to ML-based methods. This paper employs machine learning models to construct a performance evaluation scheme to predict network metrics for the Self-Clocked Rate Adaptation for Multimedia (SCReAM) algorithm. It uses a rigorous data preprocessing pipeline and a systematic application of ML methods to enhance the performance of the regression model for SCReAM’s performance metrics. Also, we constructed a dataset that provides SCReAM’s input parameters and output metrics, such as network queue delay, smoothed Round Trip Time (sRTT), and network throughput. Each prediction process has several phases: choosing the best initial regressor model, hyperparameter tuning, ensemble learning, stacking regressors, and utilizing the holdout data. Each model’s performance was evaluated through various regression metrics; this study will mainly focus on the coefficient of determination (R2) score. The improvement between the initial best-selected model and the final improved model determined that we were able to increase R2 up to 96.64% for network throughput, 99.4% for network queue delay, and 100% for sRTT.

Optimized Congestion Control Algorithm for QUIC in Wireless Networks: CubicBytes-N Algorithm

Optimized Congestion Control Algorithm for QUIC in Wireless Networks: CubicBytes-N Algorithm

A Reinforcement Learning Congestion Control Algorithm for Smart Grid Networks

A Reinforcement Learning Congestion Control Algorithm for Smart Grid Networks

Congestion tracking control of multi-bottleneck TCP networks with input-saturation and dead-zone

<abstract><p>This paper discusses the congestion control challenges in a network employing multi-bottleneck Transmission Control Protocol/Active Queue Management (TCP/AQM). The study specifically focuses on networks characterized by input nonlinearity and unknown disturbances. We regard the network as a whole, and consider the influence between multiple nodes and unknown disturbance, a dynamic model of multi-bottleneck network is established. And the impact of dead zone and saturation on the system is taken into account for the first time in the model, the builded TCP/AQM model is more practicable. Based on the characteristics of fuzzy logic systems (FLS), combined with backstepping technology and Lyapunov function, an adaptive congestion control algorithm is designed to make full use of the link resources of each node and improve the network utilization. Ultimately, the proposed algorithm's efficacy and superiority are substantiated through simulation.</p></abstract>

1177Optimized Energy Utilization in Cognitive Radio Networkswith Congestion Control

Various real-time applications can be handled through wireless sensor networks, which consist of a wide range of sensor nodes. A novel congestion control mechanism is proposed on optimized rates for energy-efficient transmissions. To reduce energy consumption across the network, a rate-based congestion control algorithm based on cluster routing is presented. By reducing the end-to-end delay, rate control improves the network life time over a large simulation period. Clustering is initially performed using novel routing algorithms. After that, rate control is implemented using an energy optimization strategy suitable for high packet delivery ratios. Finally, packets are sent with maximum throughput using regionOptimization-driven routing. The simulation is performed on the NS2 simulation platform. Finally, performances are evaluated with respect to average delay in end to end nodes, delivery ratio ofpackets, throughput, energy efficiency, energy consumption and reliability. Novel routing with an energy optimal algorithm (NREOA) reduces energy consumption as the network progresses and a variation of 20% compared with existing protocols.

TCP BBR-n: Increased throughput for wireless-AC networks

Google proposed a new TCP congestion control algorithm (CCA), Bottleneck Bandwidth and Round-trip propagation time (BBR) which has opened up new dimensions in congestion control. BBR tries to operate near Kleinrock’s operating point to avoid excessive queue formation at the bottleneck and to use the link bandwidth optimally. BBR creates a model of the network path by measuring the bottleneck bandwidth and minimum round-trip time (RTT) to maximize the delivery rate and minimize latency. BBR v2 is an updated version of BBR which addresses many shortcomings of the original BBR (BBR v1) such as interprotocol fairness, RTT fairness, and excessive retransmissions. However, BBR v2 has certain limitations in its operation in IEEE 802.11ac (Wi-Fi 5) networks. The default BBR v2 limits the throughput of Wi-Fi 5 and an increased latency has been observed. This is because the Wi-Fi 5 frame aggregation logic is underutilized and fewer frames are being sent to the Wi-Fi 5 interface. In this paper, we have proposed BBR-n (BBR new) which provides better throughput than the generic BBR v2 in the Wi-Fi 5 networks. Real-time experiments were performed over a physical testbed using Flent to confirm that BBR-n achieves over double throughput as compared to generic BBR v2 and reduced latency in networks as compared to pure loss-based variants such as Cubic and Reno.

Plausibility of BBR as CUBIC’S Replacement and proposed improvement to BBR using GENET

Recent advancements in deep reinforcement learning (RL) have opened new avenues for enhancing network congestion control algorithms. Our research builds upon these developments, particularly focusing on the BBR (Bottleneck Bandwidth and Round-trip propagation time) congestion control algorithm. We propose integrating GENET's reinforcement learning framework, a novel training paradigm that has demonstrated success in various network adaptation algorithms, including adaptive video streaming, congestion control, and load balancing. GENET leverages curriculum learning to effectively train RL models by progressively introducing more challenging network environments. This method counters the common pitfalls in RL training, such as suboptimal performance in a wide range of environments and poor generalization in narrowly defined training scenarios. Our approach exploits the strengths of GENET in identifying and emphasizing network conditions where the current RL model underperforms compared to traditional rule-based baselines, thereby facilitating significant improvements. This research aims to demonstrate that applying GENET's methodology to the BBR congestion control algorithm can yield RL policies that surpass both regularly trained RL policies and conventional baselines, thereby advancing the efficiency and reliability of network congestion control.

Evaluation of TCP Congestion Control Algorithms with traffic control policies in a PEP-based geosynchronous satellite scenario

Much work has been done in recent years comparing TCP variants in many scenarios, including the satellite one. Some of these studies were carried out with the ns-2 and ns-3 simulators, which may give a good idea of the behavior of these TCP variants, although the results cannot be considered completely realistic. Some of the latest studies use virtualization to derive a variety of measures to test the suitability of Congestion Control Algorithms in a more realistic way. Such studies also take into account new variants of TCP, such as Google’s BBR. However, few of these works have evaluated the behavior of these variants in a scenario based on Performance Enhancing Proxies. Furthermore, no emphasis has been placed on those variants of TCP that try to maximize throughput and minimize delay, which is crucial in many services with low latency requirements. Our paper aims to fill this gap, offering a comparative assessment of the performance of these three TCP flavors: BBR, YeAH and CUBIC. We have created four different approaches to mix these TCP flavors with two traffic control techniques, including Active Queue Management (AQM) policies. To perform the testings, we have developed a TCP-Splitting satellite Emulation Framework (TSEF), a platform that allows us to evaluate the performance of TCP flavors on the forward channel of a geosynchronous satellite scenario based on Performance Enhancing Proxies (PEPs). According to our results, we can conclude that the application of AQM policies and ECN marks on aggressive TCP flavors like CUBIC is mandatory to maximize throughput and minimize delay.

DECC: Achieving Low Latency in Data Center Networks With Deep Reinforcement Learning

Data Center Networks (DCNs) suffer from synchronized bursts for segmentation and aggregation modes, leading to buffer overflows at switches and increasing network delay. To overcome this problem, some congestion control algorithms like DCTCP use Explicit Congestion Notification (ECN) to notify in-network congestion and reduce switch buffer occupancy. However, the traditional Additive Increase Multiplicative Decrease (AIMD) method causes high fluctuation of round-trip time (RTT) in DCNs. Some intelligent congestion control algorithms designed for Internet can achieve great flexibility, but are not applicable in DCNs for a lack of accurate congestion feedback. In this paper, we analyze the deficiencies of utilizing RTT as congestion signals and the applicability of learning algorithms in DCNs. Then, we propose DECC, a smart TCP congestion control algorithm for DCNs, which combines Deep Reinforcement Learning (DRL) with ECN to achieve high bandwidth utilization as well as low queuing delay. DECC fully utilizes precise in-network feedback and formulates several QoS requirements to a multi-objective function. Meanwhile, it decouples cwnd adjustment with DRL decision making to gradually learn the optimal congestion control policy in real-time. We evaluate the performance of DECC in various scenarios. Simulation results show that DECC can reduce the queue length at bottleneck switches by more than 50% compared to DCTCP, while maintaining high bandwidth utilization and reducing Flow Completion Time (FCTs) under burst traffic.