Improved RTT Fairness of BBR Congestion Control Algorithm Based on Adaptive Congestion Window

Long distance high bandwidth networks are spanning several continents and many remote Healthcare centers are centralizing their data centers for economic reasons. For the best performance of their data centers, TCP (Transmission Control Protocol) performance and data security are the main critical issues in these network scenarios. TCP performance is directly related to its congestion control mechanism which is responsible for detecting and reacting to the overload traffic on the network. Data security is related to the security mechanism being used by sender and receiver nodes during communication. Linux users, which have rapidly increased in the last five years and most of the Healthcare data centers are being deployed on the Linux operating system, focus the researchers to work on Linux to enhance its performance and security accordingly. The Linux operating system uses TCP CUBIC as a congestion control mechanism with TCP during communication. TCP CUBIC became the default congestion control mechanism of Linux in 2006 after kernel 2.6.18. TCP CUBIC is fundamentally a loss based TCP congestion control mechanism and at each packet loss detection, it reduces its Congestion Window ( $cwnd$ c w n d ) size 20 percent instead of 50 percent as in trademark congestion control mechanism Standard TCP. The aim of this paper is to design a new security mechanism that will work with TCP CUBIC to achieve the maximum possible performance and security over the network link. In this paper, Network Simulator 2 (NS-2) is used to compare the performance of TCP CUBIC with state-of-the-art mechanisms in long and short Round Trip Time (RTT), high bandwidth network scenarios. Results show that when new security mechanism is used with TCP CUBIC, overall better performance in the form of protocol fairness, TCP friendliness, goodput, and convergence time is achieved over the network link.

The differentiated services (DiffServ) is proposed to provide packet level service differentiations in a scalable manner. To provide an end-to-end service differentiation to users having a connection over multiple domains, as well as a flow marker, an intermediate marker is necessary at the edge routers, and it should not be operated at a flow level due to a scalability problem. Due to this operation requirement, the intermediate marker has a fairness problem among the transmission control protocol (TCP) flows since TCP flows have intrinsically unfair throughputs due to the TCP's congestion control algorithm. Moreover, it is very difficult to resolve this problem without individual flow state information such as round trip time (RTT) and sending rate of each flow. In this paper, to resolve this TCP fairness problem of an intermediate marker, we propose a fair scalable marker (FSM) as an intermediate marker which works with a source flow three color marker (sf-TCM) operating as a host source marker. The proposed fair scalable marker improves the fairness among the TCP flows with different RTTs without per-flow management. Through the simulations, we show that the FSM can improve TCP fairness as well as link utilization in multiple domain DiffServ networks.

Google proposed a new congestion control algorithm (CCA) based on bottleneck bandwidth and round-trip propagation time (BBR), which is considered to open a new era of congestion control. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. The BBR v2 algorithm is a recently updated version by Google, which aims to improve some of the problems in the original BBR (BBRv1) algorithm, such as interprotocol fairness issues, RTT fairness issues, and excessive retransmissions. The BBRv2 evaluation results show that it can improve the coexistence with the loss_based algorithm and alleviate some of the shortcomings in BBRv1. However, when multiple BBRv2 flows enter the same link at different times, fair convergence cannot be achieved, and RTT fairness still exists. Based on these problems, we analyze the root cause and proposed an improved algorithm BBRv2+, which uses flow-aware explicit congestion notification (ECN) to quantify queue information and feedback on the accurate congestion degree. BBRv2+ algorithm can avoid blind window constraints and selectively mark packets so that different flows can converge to fairness. In the simulation experiment of Network Simulator 3 (NS3), the results show that the BBRv2+ algorithm can improve intraprotocol fairness and RTT fairness and ensure bandwidth utilization and interprotocol fairness.

Recent years internet blended multi-hop wireless networking technology experience growth in diverse sectors ranging from health care to the defense industry. An immense internet data traffic quantity depends on the transmission control protocol (TCP) due to its reliable data carrying ability between end process. TCP's congestion control algorithm performance under multi-hop wireless networks (MWN) is heavily influenced by retransmission timer (RT timer). The retransmission timeout (RTO) algorithm governs RT timer's clock duration. In MWN environment, Jacobson RTO prediction fails to endure sudden traffic delay spike that occurs due to inadequate recovery time during a link failure. In this paper, an analysis carried out to study the RTO's negative impact on TCP's performance under the multi-hop wireless scenario.

In the past few decades, many Transmission Control Protocol (TCP) congestion control algorithms have been investigated to meet the growing network demands and to enhance the performance of TCP in lossy or high-bandwidth-delay-product (high-BDP) networks. However, it is still challenging to implement a dynamic congestion control algorithm for wide range of diverse mobile users, network conditions and applications. This paper explores avenues for enhancement of TCP congestion control algorithm for next generation mobile networks by dynamically learning the available bandwidth and deriving the Congestion Control Factor N. N is used to Adaptive Increase/Adaptive Decrease (AIAD) the Congestion Window (CWND) dynamically instead of using the traditional approach that is Additive Increase/ Multiplicative Decrease (AIMD) paradigm. Once there is congestion, our proposed algorithm will not allow the CWND to decrease multiplicatively or steeply. After dropping to a certain level (lower than legacy), we try to take the CWND to previous state adaptively with the help of calculated bandwidth (based on learning). This in turn helps to efficiently control the CWND for better network utilization especially in case of lossy and high-BDP conditions. As soon as it reaches the original state, it remains stable for longer time as compared to legacy until packet loss or time out. We demonstrate the effectiveness of our algorithm with the help of live air experiments (performed in Samsung R&D India, Bangalore) and NS3 based simulation experiments. Through our experiments, we show that our algorithm outperforms the legacy congestion control algorithms (like CUBIC, RENO, TCPW) and the existing CLTCP algorithm in terms of goodput, intra algorithm fairness and inter algorithm fairness maintaining the scalability and friendliness.

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.

Quick UDP Internet connection(QUIC) protocol and bottleneck bandwidth and round-trip propagation time (BBR) based congestion control algorithm are two major contributions out of Google's persistent attempts to make the Internet faster. QUIC is an user level reliable protocol running on top of UDP to enable evolution of transport mechanisms, while BBR is designed for better TCP congestion control. Although in two different directions, they also converge with BBR support in QUIC. Many researches have proved the performance of QUIC and BBR in different network scenarios, but the evaluation in satellite Internet with long propagation delays and relatively high error rates is still missing.In this paper, we have a preliminary evaluation of the performance of QUIC with BBR via GEO satellite Internet access on dedicated network emulation testbeds. The results and our analysis confirms that for satellite Internet QUIC with the new BBR congestion control has improvements compared with the classic CUBIC.

In 2016, Google proposed the bottleneck bandwidth round-trip propagation time (BBR) as a new congestion control algorithm for improving network speed. As BBR has different behavioral characteristics from loss-based congestion control, it must ensure the coexistence between congestion control algorithms. However, throughput imbalance has been reported in a competition between BBR and loss-based congestion control. In this paper, we propose an opponent congestion control identification model of BBR (OI-BBR) with machine learning to improving inter-protocol fairness. In the emulation experiments, we confirmed that OI-BBR based on the proposed model improved the inter-protocol fairness by operating differently according to the opponent congestion control algorithms.

Classical Transmission Control Protocol (TCP) designs have never considered the identity of the competing transport protocol as useful information to TCP sources in congestion control mechanisms. When competing against a TCP flow on a bottleneck link, a User Datagram Protocol (UDP) flow can unfairly occupy the entire link bandwidth and suffocate all TCP flows on the link. If it were possible for a TCP source to know the type of transport protocol that deprives it of link access, perhaps it would be better for the TCP source to react in a way which prevents total starvation. In this paper, we use coefficient of variation and power spectral density of throughput traces to identify the presence of UDP transport protocols that compete against TCP flows on bottleneck links. Our results show clear traits that differentiate the presence of competing UDP flows from TCP flows independent of round-trip times variations. Signatures that we identified include an increase in coefficient of variation whenever a competing UDP flow joins the bottleneck link for the first time, noisy spectral density representation of a TCP flow when competing against a UDP flow in the bottleneck link, and a dominant frequency with outstanding power in the presence of TCP competition only. In addition, the results show that signatures for congestion caused by competing UDP flows are different from signatures due to congestion caused by competing TCP flows regardless of their round-trip times. The results in this paper present the first steps towards development of more ’intelligent’ congestion control algorithms with added capability of knowing the identity of aggressor protocols against TCP, and subsequently using this additional information for rate control.

Due to the development and evolution in Cognitive Radio Networks (CRNs), the performance of Transmission Control Protocol (TCP) over CRN has become a crucial factor. TCP ensures the reliable end-to-end delivery of data, which is very important for the quality of service (QoS) requirements. TCP performance is severely affected by channel switching and channel availability in CRNs. The frequent occurrence of such events is due to CRNs nature which unnecessarily reduces the transmission rate which results in degrading the overall performance. In this paper, the authors investigate the limitations of using TCP NewReno (i.e., standard TCP) over CRNs, and further propose a cross-layer-design protocol based on an interaction between transport layer and lower layers to improve TCP performance over CRNs. The design is called Cross-Layer Design for TCP (TCP-CLD). It primarily aims at improving the TCP performance over CRNs via controlling and adapting a few selected TCP parameters. Specifically, the authors propose freezing the data transmission and re-transmission timeout (RTO) of TCP during the time of channel switching and channel unavailability. In addition, the authors update the size of congestion window (cwnd) of TCP based on the bandwidth and buffer size of relay nodes. Moreover, in the case of channel switching, the authors update the Round Trip Time (RTT) of TCP based on the new route delay. Simulation experiments have been conducted using ns-2 network simulator tool whereas results show that the proposed design achieves a significant performance improvement compared with the standard TCP NewReno protocol and the other known protocol, namely, TCP-CRAHN.

In the Internet, many applications use TCP (Transmission Control Protocol) as the main transport protocol. TCP congestion control algorithm has proved to be inadequate as the speed of the transmission links increases and users demand higher throughput. The main feature of TCP congestion control algorithm is its additive increase/multiplicative decrease (AIMD) property. AIMD requires very low packet loss rates, which is not possible with the present optical transmission links, in order to achieve higher throughputs. The proposed solutions for this problem fail to protect bandwidth share of the low throughput users. We propose that low and high throughput user traffic is stored in separate queues and the two queues are served according to the weighted-round-robin (WRR) service discipline. The simulation results show that this improves the performance of TCP over high-speed links and preserves fairness to the low-throughput users.

In this paper, we aim to propose a novel transmission control protocol (TCP) congestion control method from a cross-layer-based perspective and present a deep reinforcement learning (DRL)-driven method called DRL-3R (DRL for congestion control with Radio access network information and Reward Redistribution) so as to learn the TCP congestion control policy in a superior manner. In particular, we incorporate the RAN information to timely grasp the dynamics of RAN, and empower DRL to learn from the delayed RAN information feedback potentially induced by several consecutive actions. Meanwhile, we relax the implicit assumption [that the feedback to one specific action returns at a round-trip-time (RTT) after the action is applied] in previous researches, by redistributing the rewards and evaluating the merits of actions more accurately. Experiment results show that besides maintaining a reasonable fairness, DRL-3R significantly outperforms classical congestion control methods (e.g., TCP Reno, Westwood, Cubic, BBR and DRL-CC) on network utility by achieving a higher throughput while reducing delay in various network environments.

An INT-based TCP window modulator for congestion control in data center networks

Minimum TCP throughput guarantee on minimum rate guaranteed networks

Traditional approach for data transmission in wireless networks with the shared medium, e.g., 802.11 standard family, incurs significant overheads to transmit messages in multiple source - multiple destination (MSMD) schemes. Network coding is new paradigm that increases effectiveness of transmissions in a wireless network with the shared medium and MSMD. We have considered the results of the emulation with COPE-like network coding and various Transmission Control Protocol (TCP) congestion control algorithms in wireless networks. In emulated scheme two nodes stream data to each other through bottleneck relay node. BBR TCP protocol showed the best results in this relaying scheme with enabled COPE-like network coding.