Multipath TCP Congestion Control Research Articles

Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet

In today’s digital era, the demand for uninterrupted and efficient data streaming is paramount across various sectors, from entertainment to industrial automation. While the traditional single-path solutions often fell short in ensuring rapid and consistent data transfers, Multipath TCP (MPTCP) emerges as a promising alternative, enabling simultaneous data transfer across multiple network paths. The efficacy of MPTCP, however, hinges on the choice of appropriate congestion control (CC) algorithms. Addressing the present knowledge gap, this research provides a thorough evaluation of key MPTCP CC algorithms in the context of streaming applications in open Internet environments. Our findings reveal that BALIA stands out as the most suitable choice for MPTCP streaming, adeptly balancing waiting time, throughput, and Head-of-Line blocking reduction. Conversely, the wVegas algorithm, with its delay-centric approach, proves less adequate for multipath streaming. This study underscores the imperative to fine-tune MPTCP for streaming applications, at the same time offering insights for future development areas and innovations.

SmartSBD: Smart shared bottleneck detection for efficient multipath congestion control over heterogeneous networks

Multipath TCP (MPTCP) has been widely adopted in today’s mobile devices. However, two types of congestion control algorithms, uncoupled congestion control (Uncoupled CC) and coupled congestion control (Coupled CC), cannot achieve both bottleneck friendliness and throughput maximization for both of the MPTCP subflow bottleneck sharing scenarios, shared bottleneck (SB) scenario and non-shared bottleneck (NSB) scenario, leading to performance degradation in practice. In this work, we seek to enable efficient MPTCP congestion control, by alternating between Uncoupled CC algorithms and Coupled CC algorithms via smartly detecting whether the two MPTCP subflows share the same bottleneck link. We propose SmartSBD, the first learning-based data-driven approach for shared bottleneck detection, which is accurate, adaptable, and easy-to-deploy. SmartSBD is based on the key insight that the properties of subflows that share the same bottleneck often have similar trends of variation or similar values. In the training phase, SmartSBD collects system logs when MPTCP is running in real-world heterogeneous networks, extracts features, and trains a binary classifier. In the runtime phase, SmartSBD makes periodic predictions on the bottleneck sharing condition of live MPTCP subflows, and uses the prediction results to alternate between Coupled CC and Uncoupled CC. Our evaluations demonstrate that SmartSBD outperforms existing approaches.

Managing Energy Consumption of Devices with Multiconnectivity by Deep Learning and Software-Defined Networking.

Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes.

Learning to Harness Bandwidth With Multipath Congestion Control and Scheduling

Multipath TCP (MPTCP) has emerged as a facilitator for harnessing and pooling available bandwidth in wireless/wireline communication networks and in data centers. Existing implementations of MPTCP such as, Linked Increase Algorithm (LIA), Opportunistic LIA (OLIA) and BAlanced LInked Adaptation (BALIA) include separate algorithms for congestion control and packet scheduling, with pre-selected control parameters. We propose a Deep Q-Learning (DQL) based framework for joint congestion control and packet scheduling for MPTCP. At the heart of the solution is an intelligent agent for interface, learning and actuation, which learns from experience optimal congestion control and scheduling mechanism using DQL techniques with policy gradients. We provide a rigorous stability analysis of system dynamics which provides important practical design insights. In addition, the proposed DQL-MPTCP algorithm utilizes the ‘recurrent neural network’ and integrates it with ‘long short-term memory’ for continuously i) learning dynamic behavior of subflows (paths) and ii) responding promptly to their behavior using prioritized experience replay. With extensive emulations, we show that the proposed DQL-based MPTCP algorithm outperforms MPTCP LIA, OLIA and BALIA algorithms. Moreover, the DQL-MPTCP algorithm is robust to time-varying network characteristics, and provides dynamic exploration and exploitation of paths.

Toward the Implementation of MPTCP Over mmWave 5G and Beyond: Analysis, Challenges, and Solutions

5G and beyond 5G networks are going to be an inseparable part of human lives in the future. They will dominate any aspects of everyday activities from smart homes, remote surgery, and smart cities to autonomous driving, which demand high throughput through low latency end-to-end communication. Consequently, employing higher frequencies in millimeter-wave for the coming cellular networks will be inevitable. On the one hand, millimeter-wave can provide massive data rates; on the other hand, it suffers from some shortcomings such as blockage and misalignment that can occur because of the susceptible characteristic of the high frequencies. These flaws can mislead TCP in adjusting its sending rate efficiently and reduce the connection quality that a user discerns. Deploying Multipath TCP (MPTCP) is one of the schemes that can relieve the aforementioned issues and assist in utilizing the new generation mobile networks’ high potential by leveraging its features in exploiting diverse paths in networks such as NR or LTE. A well-designed MPTCP can select the best path among the available ones, so it can enhance the perceived user experience. This paper fills the gap of having a comprehensive overview of MPTCP and its deployment over 5G and beyond 5G networks. It analyses millimeter-wave-based cellular networks, MPTCP procedures and parameters, state-of-the-art MPTCP congestion control mechanisms and schedulers, and the probable solutions that may enhance the protocol’s functionality in cellular communication.

Multipath Congestion Control: Measurement, Analysis, and Optimization From the Energy Perspective

Multipath TCP (MPTCP) is a promising TCP extension that exploits different Internet paths between a pair of hosts to obtain high aggregate throughput, while it also brings the concern of energy consumption. There has been a lively interest in the design of energy-efficient MPTCP. The research community, however, lacks a comprehensive understanding of which components in an MPTCP congestion control algorithm play the fundamental role in energy efficiency, how various algorithms compare against each other from energy-consuming perspective, or whether there exist potentially better solutions for energy saving. This paper takes the first step towards answering these questions. Through realworld MPTCP Linux kernel experiments, we show that the energy consumption is related to three major aspects: average throughput, path delay and different network scenarios. In order to bridge congestion control to the three aspects, we analyze the existing algorithms and capture the essential parameters of multipath congestion control model related to energy efficiency. Then we propose a window increase factor to shift traffic to low-delay energy-efficient paths. To further extend this design, we apply an energy-aware compensative parameter to fit the general hierarchical Internet topology. Our evaluation indicates that the enhanced congestion control module can successfully improve MPTCP energy efficiency without affecting its transmission performance.

An Analysis of MPTCP Congestion Control

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.

Choosing a Proper Control Strategy for Multipath Transmission in Industry 4.0 Applications

The industrial applications, especially in the context of communication-intensive Industry 4.0, require high throughput, good robustness, and low latency in the data exchange among the system components. These objectives can be achieved through multipath transfer. In order to overcome the restrictions of the primary networking protocols with respect to engaging multiple network interfaces, a new version of transmission control protocol (TCP) has recently been standardized—multipath TCP (MPTCP). Still though, only one type of congestion control algorithm is used to manage the flow of data despite various choices available in the modern operating systems, which leads to nonoptimal transfer dynamics. In this article, the influence of TCP congestion control algorithms, operating at the path and MPTCP level, on the transmission quality in industrial applications is examined. The conducted experiments involving physical devices and real, public networks lead to a set of recommendations for the algorithm selection so that the premises of robustness and efficiency are met. The default algorithm configuration is found ill-suited, yielding priority to nonhomogeneous settings. Only a few algorithms can maintain the benefits of multiplicity for an on-going transmission when the transfer conditions at one or more paths deteriorate. Motivated by the deficiency of common solutions, a new MPTCP congestion control algorithm has been developed. While retaining their good properties in the general networking context, this new–robust–algorithm yields improved performance in the uncertain, industrial communication environment.

ECN-based shared bottleneck detection for multi-path TCP

Multi-Path TCP (MPTCP) shows its unique advantages over single-path TCP thanks to its aggregate throughput from multiple paths, balancing efficiency and fairness over multipath transmission. However, MPTCP senders should tackle the friendliness issue carefully when sharing the same bottleneck with single-path TCP. Unfortunately, existing shared bottleneck detection schemes either utilize end-to-end delay without consideration of multiple-bottleneck scenarios, or identify subflows on switches with large operation overhead. In this paper, we propose a lightweight yet accurate approach, ECN-based MPTCP (EMPTCP), to detect shared bottleneck of multiple subflows. EMPTCP uses the widely deployed ECN scheme to capture the real congestion state of shared bottlenecks, while at the same time is compatible with various MPTCP congestion control algorithms. Through model analysis, simulation test and real experiment, we show that EMPTCP achieves higher than 85% accuracy in shared bottleneck detection, thus improving the network efficiency and fairness.

Deep Reinforcement Learning for Handover-Aware MPTCP Congestion Control in Space-Ground Integrated Network of Railways

Space-ground integrated networks (SGINs) have been recently regarded as a promising way to provide resilient, dependable as well as efficient data transmission in the high-speed railway (HSR) scenario. Applying multipath transmission control protocol (MPTCP) to SGIN can realize data transmission simultaneously via terrestrial and satellite networks. However, since the existing congestion control (CC) mechanisms of MPTCP fail to distinguish between adverse influences (such as packet loss and/or round-trip time increase) caused by congestion and those caused by handovers, it suffers severe performance degradation in the HSR scenario where handover frequently occurs. In this article, we first present the SGIN oriented HSR (SGIN-HSR) with MPTCP. Then leveraging cross-layer information (i.e., reference signal received power), we design a novel cross-layer aided MPTCP CC mechanism targeted at SGIN-HSR based on deep reinforcement learning, which is referred to as HSR-CC, to alleviate performance degradation problems induced by handover. The experimental results show that HSR-CC significantly enhances the goodput and outperforms state-of-the-art MPTCP CC algorithms in SGIN-HSR environments where handover frequently occurs.

DeepCC: Multi-Agent Deep Reinforcement Learning Congestion Control for Multi-Path TCP Based on Self-Attention

With the development of the Internet of Things (IoT) and 5G, there are ubiquitous smart devices and network functions providing emerging network services efficiently and optimally through building many network connections based on WiFi, LTE/5G, Ethernet, and etc. The Multipath TCP (MPTCP) protocol that enables these devices to establish multiple paths for simultaneous data transmission, has been a widely used extension of standard TCP in smart devices and network functions. On the other hand, more heavy and time-varying traffic loads are generated in an MPTCP network, so that an efficient congestion control mechanism that schedules the traffic between multiple subflows and avoids congestion is highly required. In this paper, we propose a decentralized learning approach, DeepCC, to adapt to the volatile environments and realize the efficient congestion control. The Multi-Agent Deep Reinforcement Learning (MADRL) is used to learn a policy of congestion control for each subflow according to the real-time network states. To deal with the problem of the fixed state space and slow convergence, we adopt two self-attention mechanisms to receive the states and train the policy, respectively. Due to the asynchronous design of DeepCC, the learning process will not introduce extra delay and overhead on the decision-making process. Experiment results show that DeepCC consistently outperforms the well-known heuristic method and DRL-based MPTCP congestion control method in terms of goodput and jitter. Besides, DeepCC with the attention mechanism reduces convergence time by about 50% and increase goodput by about 80% compared with the commonly used structures of neural networks.

D-OLIA: A Hybrid MPTCP Congestion Control Algorithm with Network Delay Estimation.

With the recent evolution of mobile technology, modern devices equipped with multiple communication interfaces have become popular. The multipath transmission control protocol (MPTCP) has evolved to facilitate multiple communication interfaces through a single TCP connection for faster Internet access. MPTCP congestion control algorithms (MPTCP-CCAs) control data flow by fulfilling three design goals, i.e., ensuring improvement over single-path flows, ensuring fairness, and balancing congestion. Current MPTCP-CCAs cannot fulfill these design goals. For example, the opportunistic-linked increase algorithm (OLIA), a well-known MPTCP-CCA in load balancing, often results in low throughput because it cannot properly utilize the underlying network. In addition, the current Internet has a rapidly changing characteristic due to a large amount of short-lived traffic, making it difficult for MPTCP-CCAs to cope. An awareness of prevailing network delay conditions might help MPTCP-CCAs to utilize the network capacity fully. Therefore, we propose dynamic OLIA (D-OLIA), a hybrid MPTCP-CCA that enhances the performance of OLIA by integrating an awareness of the current network delay condition for deciding the congestion window (CWND) decrease factor. We estimate the current network delay condition, i.e., less-congested or congested, by observing the changes in the round-trip-time (RTT). Based on the estimated network delay condition, we decide the CWND decrease factor in real-time for reducing the CWND during packet loss events. We implemented D-OLIA in the Linux kernel and experimented using the Mininet emulator. The emulation results demonstrate that D-OLIA successfully estimates current network delay conditions and results in approximately a 20% increased throughput compared to the original OLIA. Compared to certain MPTCP-CCAs, it also yields a highly improved performance in terms of throughput, RTT, packet retransmissions, and fairness among the MPTCP sub-flows.

Reinforcement Learning-Based Mobile AR/VR Multipath Transmission With Streaming Power Spectrum Density Analysis

Multi-path transmission control protocol (MPTCP) is an extension of TCP that enables the concurrent transmission of information through different network interfaces (e.g., Cellular, Wi-Fi, 802.11p, and so on) available at terminal side. It is well known that MPTCP can provide significant advantages in bandwidth aggregation and transmission stability. Unfortunately, path diversity can limit bandwidth aggregation efficiency and incur higher delays. These issues become critical when in presence of emerging mobile AR and VR applications, which are bandwidth hungry, time-sensitive and exhibit abrupt variations of the bitrate. To address these issues, we propose the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> einforcement <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning-based mobile AR/VR multipath transmission with streaming <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> ower <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> pectrum <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</u> ensity analysis (RL-PSD). RL-PSD analyses the Power Spectrum Density (PSD) of the AR/VR input stream to extract its features. Then, both the input stream and network features are considered to model the MPTCP congestion control as an reinforcement learning process. Finally, a two-stage reinforcement algorithm is proposed to optimize transmission performance. RL-PSD has been tested in both single-terminal and multi-terminal scenarios: results show that it outperforms the other advanced solutions conceived to support the multipath transmission of AR/VR streams.

Adaptive Decrease Window for BALIA (ADW-BALIA): Congestion Control Algorithm for Throughput Improvement in Nonshared Bottlenecks

The main design goals of the multipath transmission control protocol (MPTCP) are to improve the throughput and share a common bottleneck link fairly with a single-path transmission control protocol (TCP). The existing MPTCP congestion control algorithms achieve the goal of fairness with single-path TCP flows in a shared bottleneck, but they cannot maximize the throughput in nonshared bottlenecks, where multiple subflows traverse different bottleneck links. This is because the MPTCP is designed not to exceed the throughput of a single-path TCP competing in the bottleneck. Therefore, we believe that MPTCP congestion control should have different congestion window control mechanisms, depending on the bottleneck type. In this paper, we propose an adaptive decrease window (ADW) balanced linked adaptation (BALIA) congestion control algorithm that adaptively adjusts the congestion window decrease in order to achieve better throughput in nonshared bottlenecks while maintaining fairness with the single-path TCP flows in shared bottlenecks. The ADW-BALIA algorithm detects shared and nonshared bottlenecks based on delay fluctuations and it uses different congestion window decrease methods for the two types of bottleneck. When the delay fluctuations of the MPTCP subflows are similar, the ADW-BALIA algorithm behaves the same as the existing BALIA congestion control algorithm. If the delay fluctuations are dissimilar, then the ADW-BALIA algorithm adaptively modulates the congestion window reduction. We implement the ADW-BALIA algorithm in the Linux kernel and perform an emulation experiment that is based on various topologies. ADW-BALIA improves the aggregate MPTCP throughput by 20% in the nonshared bottleneck scenario, while maintaining fairness with the single-path TCP in the shared bottleneck scenario. Even in a triple bottleneck topology, where both types of bottlenecks exist together, the throughput increases significantly. We confirmed that the ADW-BALIA algorithm works stably for different delay paths, in competition with CUBIC flows, and with lossy links.

DEFT: Multipath TCP for High Speed Low Latency Communications in 5G Networks

Multipath TCP (MPTCP) is a promising solution that can provide high end-to-end throughput in fifth generation (5G) mobile networks. Many next-generation applications will require high throughput and low latency simultaneously, but the current MPTCP congestion control algorithms cannot reliably satisfy this requirement. In this paper, a novel MPTCP congestion control scheme named delay-equalized FAST (DEFT) is proposed to achieve high throughput and low end-to-end (E2E) delay in 5G networks. First, in order to achieve high throughput, DEFT includes a novel window control algorithm that shows fast responsiveness when the state of the millimeter-wave (mmWave) link changes from line-of-sight (LOS) to non-LOS (NLOS) and vice versa. Second, in order to achieve low E2E delay, DEFT includes a delay-equalizing algorithm which minimizes additional reordering delay in the receive buffer. The performance of DEFT was evaluated based on ns-3 simulation and was compared with wVegas, Balia, and delay-adapted LIA. Simulation results show that DEFT can provide a significant goodput gain and application-level E2E delay reduction for the range of interest.

A Software Defined Network Based Fuzzy Normalized Neural Adaptive Multipath Congestion Control for the Internet of Things

Multipath Transmission Control Protocol (MPTCP) enables multi-homed devices to establish multiple simultaneous routes for data transmission. Congestion Control (CC) is a fundamental mechanism for implementing and designing MPTCP. The Internet of Things (IoT) networks generate a massive volume of heterogeneous traffic with high dimensional states and diverse QoS characteristics. The existing MPTCP CC algorithms are unable to perform efficiently under highly mobile and dynamic IoT environments. We propose a novel model-free SDN-based adaptive actor-critic deep reinforcement learning framework based on a fuzzy normalized neural network to address the issue of CC for MPTCP in the IoT networks. In the proposed method, an agent can learn efficiently and better approximate the state-action value function of the actor and the action function of the critic to adjust the sub-flows congestion windows size adaptively according to the dynamic condition of a network. Simulation results show that the proposed scheme outperforms the state of the art schemes in terms of the goodput under highly-dynamic IoT environments.

Optimal Load Balancing Linked Increased Algorithm for Multipath TCP

The most commonly used mechanism for establishing a connection between two communicating entities is transmission control protocol (TCP) that provides a reliable connection. Due to the latest developments in communication technologies, one can use multiple connections in a network using Wi-Fi, LTE/HSPA, etc. Since the single path TCP (SPTCP) provides a single connection at a time, it cannot take the benefit of multiple connections. The multipath TCP (MPTCP) can this problem by dividing a flow into multiple subflows; each may use a different network connection. Thus, it has a great capability to achieve high throughput in comparison to the SPTCP. The existing MPTCP congestion control algorithms have different issues like TCP-friendliness, responsiveness, and load balancing. To address these issues, this paper proposes a new MPTCP congestion control algorithm, named as optimal load balancing linked increased algorithm (OLBLIA), that considers the congestion window corresponding to a path that is least congested. The window corresponding to the least congested path is increased with maximum size (i.e., nearly equal to the congestion window of SPTCP in the best path) that makes it more responsive and friendlier towards the SPTCP. Experimentally and analytically it is shown that the proposed OLBLIA resolves the above mentioned issues and outperforms the existing MPTCP’s congestion control algorithms.

SmartCC: A Reinforcement Learning Approach for Multipath TCP Congestion Control in Heterogeneous Networks

The Multipath TCP (MPTCP) protocol has been standardized by the IETF as an extension of conventional TCP, which enables multi-homed devices to establish multiple paths for simultaneous data transmission. Congestion control is a fundamental mechanism for the design and implementation of MPTCP. Due to the diverse QoS characteristics of heterogeneous links, existing multipath congestion control mechanisms suffer from a number of performance problems such as bufferbloat, suboptimal bandwidth usage, etc. In this paper, we propose a learning-based multipath congestion control approach called SmartCC to deal with the diversities of multiple communication path in heterogeneous networks. SmartCC adopts an asynchronous reinforcement learning framework to learn a set of congestion rules, which allows the sender to observe the environment and take actions to adjust the subflows’ congestion windows adaptively to fit different network situations. To deal with the problem of infinite states in high-dimensional space, we propose a hierarchical tile coding algorithm for state aggregation and a function estimation approach for $Q$ -learning, which can derive the optimal policy efficiently. Due to the asynchronous design of SmartCC, the processes of model training and execution are decoupled, and the learning process will not introduce extra delay and overhead on the decision making process in MPTCP congestion control. We conduct extensive experiments for performance evaluation, which show that SmartCC improves the aggregate throughput significantly and outperforms the state-of-the-art mechanisms on a variety of performance metrics.

When High-Speed Railway Networks Meet Multipath TCP: Supporting Dependable Communications

Multipath TCP (MPTCP) is an extraordinarily promising technology to guarantee service dependability in high-speed railway (HSR) networks. However, the phenomenon that wireless losses caused by frequent handoffs or other non-congestion losses in HSR networks has a detrimental influence on the performance of MPTCP. In order to improve performance of MPTCP when Long-Term Evolution for Railway (LTE-R) and WiFi communication routes are aggregated in HSR networks, we present a novel congestion control scheme, i.e., RVeno in this letter. Simulation results clearly reveal RVeno outperform the existing MPTCP congestion control algorithms in terms of throughput.

Experience-Driven Congestion Control: When Multi-Path TCP Meets Deep Reinforcement Learning

In this paper, we aim to study networking problems from a whole new perspective by leveraging emerging deep learning, to develop an experience-driven approach, which enables a network or a protocol to learn the best way to control itself from its own experience (e.g., runtime statistics data), just as a human learns a skill. We present design, implementation and evaluation of a deep reinforcement learning (DRL)-based control framework, DRL-CC (DRL for Congestion Control), which realizes our experience-driven design philosophy on multi-path TCP (MPTCP) congestion control. DRL-CC utilizes a single (instead of multiple independent) agent to dynamically and jointly perform congestion control for all active MPTCP flows on an end host with the objective of maximizing the overall utility. The novelty of our design is to utilize a flexible recurrent neural network, LSTM, under a DRL framework for learning a representation for all active flows and dealing with their dynamics. Moreover, we, for the first time, integrate the above LSTM-based representation network into an actor-critic framework for continuous (congestion) control, which leverages the emerging deterministic policy gradient to train critic, actor, and LSTM networks in an end-to-end manner. We implemented DRL-CC based on the MPTCP implementation in the Linux kernel. The experimental results show that 1) DRL-CC consistently and significantly outperforms a few well-known MPTCP congestion control algorithms in terms of goodput without sacrificing fairness, 2) it is flexible and robust to highly-dynamic network environments with time-varying flows, and 3) it is friendly to regular TCP.