Multipath Congestion Control Research Articles

Breaking the Inertial Thinking: Non-Blocking Multipath Congestion Control Based on the Single-Subflow Reinforcement Learning Model

Breaking the Inertial Thinking: Non-Blocking Multipath Congestion Control Based on the Single-Subflow Reinforcement Learning Model

Adaptive Multi-Source Multi-Path Congestion Control for Named Data Networking

Adaptive Multi-Source Multi-Path Congestion Control for Named Data Networking

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.

Achieving Flexible and Lightweight Multipath Congestion Control Through Online Learning

The upgrade of network devices to be equipped with multiple network interfaces makes it possible to improve network throughput performance through multipath transmission protocols, especially multipath TCP (MPTCP). However, so far the mostly used MPTCP protocols have a common limitation, namely the rigid and conservative method. They have been designed with little consideration of the fact that real networks are dynamic and the network status changes frequently, thus leading to the poor performance of current MPTCP in many realistic scenarios. In this paper, we propose a lightweight multipath congestion control algorithm based on online learning, named MP-OL. MP-OL models congestion control as a multi-armed bandit problem, and adjusts the sending rate of each subflow flexibly and adaptively through online learning. Therefore, MP-OL possesses the capability of suiting various network scenarios, and can achieve fairness and high performance in dynamic network environment. It can also flexibly switch between online learning and traditional method, which reduces the computational complexity while ensuring the learning efficiency, thus making MP-OL easy to deploy and use. As the experimental results demonstrated, compared with the leading MPTCP variants, MP-OL achieves significant improvements in fairness and link utilization, and shows better resilience to non-congestion loss and better adaptability to unstable network conditions. In real networks, MP-OL also obtains better throughput performance.

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.

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.

Adaptive QoS-aware multipath congestion control for live streaming

Nowadays, the popularization of live streaming bring challenges in providing customized Quality of Service (QoS) and satisfying diverse Quality of Experience (QoE) requirements. Multipath TCP (MPTCP), designed for bandwidth and reliability in aggregating transmission, has the potential to improve live streaming performance with multiple simultaneously transmitting paths. However, the existing multipath congestion control algorithms (CCAs) of MPTCP fail to adapt to diverse network environments and different QoS requirements. We study the problem with extensive experiments to observe the limitations of current multipath CCAs. To tackle these problems and support stable and customized live streaming, we propose ACCeSS, an adaptive QoS-aware multipath congestion control framework. ACCeSS is able to promptly adapt to network changes and QoS requirements with a novel control policy optimization phase. In order to adjust and stimulate improvement on the preferred performance metric, ACCeSS exploits Random Forest Regressing (RFR) method to perform QoS-specific utility function optimization. Besides, ACCeSS is implemented and deployed in a multipath live streaming system. We compare it with other multipath CCAs in the Linux kernel and evaluate their performance in both emulated and real-world networks. It is revealed that ACCeSS outperforms classic multipath CCAs and the state-of-the-art learning-based multipath CCA, with better environment adaptive capability of QoS and higher QoE for live streaming.

Congestion and Computer Program Control Algorithm Strategy for Wireless Sensor Networks Based on Cloud Model

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.

Delay-Based Network Utility Maximization Modelling for Congestion Control in Named Data Networking

Content replication and name-based routing lead to a natural multi-source and multipath transmission paradigm in NDN. Due to the unique connectionless characteristic of NDN, current end-to-end multipath congestion control schemes (e.g. MPTCP) cannot be used directly on NDN. This paper proposes a Network Utility Maximization (NUM) model to formulate multi-source and multipath transmission in NDN with in-network caches. From this model, a family of receiver-driven transmission solutions can be derived, named as path-specified congestion control. The path-specified congestion control enables content consumers to separate the traffic control on each path, which consequently facilitates fair and efficient bandwidth sharing amongst all consumers. As a specific instance, a Delay-based Path-specified Congestion Control Protocol (DPCCP) is presented, which utilizes queuing delays as signals to measure and control congestion levels of different bottlenecks. In addition, a set of high-performance congestion control laws are designed to accelerate bandwidth and fairness convergence towards the optimum defined by the NUM model. Finally, DPCCP is compared with state-of-the-art solutions. The experimental evaluations show that DPCCP outperforms existing solutions in terms of bandwidth utilization, convergence time and packet loss.

Cross‐layer multipath congestion control, routing and scheduling design in ad hoc wireless networks

Demands for stable and efficient transmission are increasing due to the ongoing expansion of global wireless data traffic. One of the most promising ways to moderate burden of traffic load and decrease the queuing delay is to design cross-layer protocols. This paper considers a utility maximization problem for multipath ad hoc wireless networks via joint cross-layer congestion control, routing and scheduling design. In the formulated utility maximization problem, rate, scheduling and queuing delay constraints are involved under the condition of fixed channels and time varying channels. To solve this utility maximization problem, two scheduling methods are proposed, that is, perfect scheduling and distributed scheduling. In perfect scheduling, all nodes in the network contribute to the scheduling process, while distributed method only takes into account adjacent nodes. For both methods, the global convergence is proved. A comprehensive simulation evaluation is performed which shows that this proposed algorithms outperform existing cross layer protocols in terms of increasing source rate and reducing congestion price.

LIA-EN: enhancing the performance of multipath congestion control over lossy networks

LIA-EN: enhancing the performance of multipath congestion control over lossy networks

LIA-EN: enhancing the performance of multipath congestion control over lossy networks

Congestion control provides the endpoint the ability to probe available bandwidth and prevents the network falling into severe congestion. But loss based congestion control algorithms designed for ...

Multi-Path Selection and Congestion Control for NDN: An Online Learning Approach

In Named Data Networking (NDN) architecture, data can be obtained from multiple content sources (i.e., producers or caching nodes) with multiple paths, making the traditional end-to-end (i.e., TCP/IP) congestion control scheme invalid. In addition, the NDN multi-path discovery and management are still an open issue as the dynamic network topology changes. In this article, we propose a multi-path congestion control mechanism, named MPCC, which includes two major components, i.e., multi-path discovery and multi-path congestion control. Particularly, for multi-path discovery, we first devise a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">path tag</i> to uniquely mark each sub-path in the forwarding process, and then propose a tag-aware forwarding strategy to discover and manage sub-paths. For multi-path selection and congestion control, we first integrate the metrics of packet loss, bandwidth, round trip time, and path centrality, for path assessment, based on which, we then leverage the Upper Confidence Bound (UCB) algorithm to select sub-paths in order to maximize the network throughput. In addition, for selected sub-paths, we have devised a sub-path window adaptation algorithm to avoid multi-path congestions. At last, we implement MPCC in ndnSIM and conduct extensive experiments for performance evaluation. Our results demonstrate that MPCC can discover all sub-paths in real-time for the multi-path scenario, and can effectively avoid multi-path congestions with improving throughput and reducing transmission time.

A solution to MPTCP’s inefficiencies under the incast problem for Data Center Networks

In recent years several multipath data transport mechanisms, such as MPTCP and XMP, have been introduced to effectively exploit the path diversity of data center networks (DCNs). However, these multipath schemes have not been widely deployed in DCNs. We argue that two key factors among others impeded their adoption: TCP incast and minimum window syndrome . First, these mechanisms are ill-suited for workloads with a many-to-one communication pattern, commonly found in DCNs, causing frequent TCP incast collapses. Second, the syndrome we discover for the first time, results in 2-5 times lower throughput for single-path flows than multipath flows, thus severely violating network fairness.To effectively tackle these problems, we propose AMP: an adaptive multipath congestion control mechanism that quickly detects the onset of these problems and transforms its multipath flow into a single-path flow. Once these problems disappear, AMP safely reverses this transformation and continues its data transmission via multiple paths. Our evaluation results under a diverse set of scenarios in a fat-tree topology with realistic workloads demonstrate that AMP is robust to the TCP incast problem and improves network fairness between multipath and single-path flows significantly with little performance loss.

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.

Low Latency Friendliness for Multipath TCP

Efficient congestion control is critical to the operation of MPTCP, the Multipath extension of TCP. Congestion control in such an environment primarily aims at enhancing the cumulative TCP throughput over the available paths, while preserving TCP-friendliness by fairly sharing the available bandwidth with single-path TCP flows in each path. While most existing multipath congestion control algorithms fulfill the TCP-friendliness objective in their steady state, their throughput convergence latency is high, rendering them ineffective for short-lived flows. We have proposed Normalized Multipath Congestion Control (NMCC), an MPTCP congestion control algorithm that achieves TCP-friendliness faster, by normalizing the growth of individual sub-flow throughput rather than the throughput itself. As NMCC can become unfriendly when it experiences sparse congestion events, in this paper we introduce the extended NMCC (e-NMCC) protocol that caters for TCP-friendliness upon both throughput growth and throughput reduction epochs. We analytically characterize e-NMCC in terms of TCP-friendliness and responsiveness and compare it with alternative algorithms. Finally, we assess the performance of e-NMCC through experimentation with the htsim simulator and a real Linux implementation. Our results confirm that e-NMCC accelerates throughput convergence, thus ensuring TCP-friendliness regardless of connection duration and underlying network conditions.

A Multipath Packet Scheduling Approach based on Buffer Acknowledgement for Congestion Control

Abstract Congestion is an important concern in both wired and wireless communication systems. Several technologies are in use for the data link, network, and transport layer to manage congestion. Conventional congestion control approaches are unreliable for transport layer protocols by means of congestion control mechanisms utilized for multimedia streaming. Packets that schedule flow control to employ multiple network interfaces to smoothly transmit streaming data play an important role in congestion control by overwhelming the intermediate node’s buffer and causing packet loss. In this paper, we propose a Multipath Packet Scheduling (MP-PS) approach to efficiently stream the data packets in multiple paths. It consists of a Multi-Path Packet Scheduler (MPS) to perform the path scheduling based on the path scheduling delay probability, and a Multipath Congestion Control (MCC) to regulate the congestion control through an explicit acknowledgment for the rate of transmission control based on packet delivery rate and buffer availability. It aims to reduce packet loss and improve the throughput utilizing the MP-PS approach. The proposed approach has been extensively simulated by means of a simulator and it showed improvised performance in provisions of throughput and buffer utilization when evaluated with the conventional approach.

An Upward Max-Min Fairness Multipath Flow Control

Many rate allocation algorithms for multipath flows which satisfy max-min fairness are centralized and not scalable. Upward max-min fairness is a well-known relaxation of max-min fairness and can be achieved by an algorithm extended from water-filling algorithm. In this paper, we propose a price-based multipath congestion control protocol whose equilibrium point satisfies upward max-min fairness. Our protocol is derived from a network utility maximization model for multipath flows.

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.

Shared Bottleneck Detection Based on Congestion Interval Variance Measurement

The key technical issue of bottleneck fairness-based multipath congestion control is to detect whether two flows share a common bottleneck or not. Previous approaches perform poorly when the delay of the two paths from the shared bottleneck to the common receiver differs significantly (named the path lag problem). In this letter, we propose and implement a shared bottleneck detection scheme based on congestion interval variance measurement (SBDV). Our scheme uses only one-way delay measurement within each flow to detect whether two flows share a bottleneck or not. If the variance of time interval between the two flows experiencing congestion is smaller than a threshold, which is determined by the duration of congestion, the two flows can be considered to share a common bottleneck. Otherwise, they are considered to have different bottlenecks. Our simulation shows that the accuracy of SBDV is high in most of the experiments, even when the bottlenecks are partially overlapped, and our scheme is robust to the troublesome Path lag problem, as compared to other state-of-the-art techniques.