Multipath TCP Research Articles

Combining 5G New Radio, Wi-Fi, and LiFi for Industry 4.0: Performance Evaluation.

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi -complementary WATs in the 5G-CLARITY project-in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms.

MPTCP-based link congestion detection and traffic control scheme in software-defined networks

Multipath TCP (MPTCP) is one of the leading transport layer protocols that enables multipath transmission to improve network performance. However, MPTCP can experience throughput degradation due to link congestion, which causes underutilizing of the network capacity. We proposed a new link congestion detection based on Software-Defined Networking (SDN) for MPTCP that detects and detours link congestion to achieve a stable MPTCP performance. Measurement results show that the proposed scheme enables efficient packet transmission by minimizing the effect of link congestion. Moreover, a combination of the proposed scheme and SDN monitoring enables dynamic traffic control based on the proposed link congestion detection.

Enhanced Multipath TCP to Improve the Mobile Device Network Resiliency

Enhanced Multipath TCP to Improve the Mobile Device Network Resiliency

An Evaluation of Multipath TCP with Highly Asymmetric Subflows

An Evaluation of Multipath TCP with Highly Asymmetric Subflows

Enhancing MPTCP performance in wireless networks with PRS-MPTCP

In recent years, there has been a significant increasing demand for wireless networks, particularly mobile communication. Additionally, smart devices like mobile phones and tablets are able to use multiple interfaces simultaneously. In this regard, the concept of Multipath TCP (MPTCP) has been introduced, enabling the utilization of multiple interfaces for concurrent communication. However, packet loss and subflow heterogeneity, especially in wireless networks, cause an increase in Out-of-Order (OfO) packets at the receiver node, which leads to a decrease in the total MPTCP throughput. To address these performance-related challenges, numerous schedulers have been proposed. However, most existing methods have primarily focused on improving performance without adequately considering the impact of packet loss. This research paper provides a comprehensive overview of MPTCP schedulers. Subsequently, we propose a Practical and Robust Scheduler for MPTCP (PRS-MPTCP) with the specific aim of minimizing OfO packets to improve MPTCP performance in wireless systems. The PRS-MPTCP scheduler takes into account various characteristics of each subflow, including RTT, CWND, and the number of packet losses, to effectively decide which packets should be assigned to which subflow. By using Mininet-WiFi emulation, fair comparisons between PRS-MPTCP and the state-of-the-art schedulers have been conducted, considering throughput, the number of OfO packets, and retransmission rates as performance metrics. The evaluation results reveal the profound impact of selected parameters on the behavior of the schedulers and the overall performance of MPTCP. Ultimately, the results demonstrate that PRS-MPTCP guarantees acceptable throughput and achieves lower retransmission rates and fewer OfO packets compared to other methods. In the PRS-MPTCP, the number of OfO packets has decreased by 38 %, 37 %, 45 %, and 44 % compared to BLEST, ECF, RR, and the Default scheduler, respectively.

Design and implementation for a UAV-based streaming media system

The Unmanned Aerial Vehicle (UAV)-based video streaming transmission is critical for real-time communication (RTC) applications in flying ad-hoc networks (FANET). The communication links between the nodes, however, are often unstable, especially in harsh network environments. This article first presents an architecture of a UAV-based streaming media system (USMS) for more reliable transmission, where the UAVs play multiple roles simultaneously in terms of video capturing, encoding, transmission, caching, decoding, and evaluation. To promote fluency of video transmission, we propose a distributed algorithm to explore the Tradeoff between the bandWidth, the Load, and the Video parameters (TWLV) based on the UAV flying status and Quality of Service (QoS). To enhance the Quality of Experience (QoE) significantly, we design an application-oriented multi-link transmission system with a novel multi-module stacking technology. Leveraging this design, we implement an extended splitting-merging streaming (ESMS) algorithm to deal with heavy payloads and reduce transmission latency. We also propose a multi-link retransmission (MLR) algorithm to eliminate consecutive packet losses and achieve more efficient video rebuffering. Sufficient experimental results verify that compared with the commercially available products that utilize application-layer forward error correction (AL-FEC), MultiPath TCP (MPTCP), and Deadline-aware Transport Protocol (DTP), our proposed USMS outperforms in throughput promotion, latency reduction, congestion control, packet recovery, and rebuffering efficiency.

Freshness-Aware Age Optimization for Multipath TCP Over Software Defined Networks

Recently increasing real-time network applications have raised a new requirement for the future 6G network transmission, which desires the information-update packets arrive at the receiver as timely as possible. Multipath TCP (MPTCP) can be employed in the real-time update transmission system by making use of multiple paths so as to promote the information status update. However, current optimization solutions of MPTCP focus on traditional transmission performance such as throughput, delay, loss, jitter, etc. None of these works considers the freshness metric for MPTCP to satisfy the real-time demands. What's more, most works about transmission model in Software Defined Network (SDN) are based on packet-level analysis without considering flow-level situation. In order to fill these gaps, this paper proposes a novel freshness-aware age optimization solution for MPTCP (FAMT) over SDN. FAMT introduces a new four-tuples to describe the transmission state in the SDN architecture. Then FAMT classifies the whole transmission process into two phases: connection establish phase and data transmission phase. Based on the two phases, FAMT evaluates and defines the age of information for different situations. In addition, FAMT designs an innovative age-assisted fluid model of MPTCP which can realize the four goals effectively.

TCP and MP-TCP in mmWave 5G Networks

Future 5G networks will likely include mmWave radio access communication links, because of their potential multi-gigabit-per-second capacity. However, these frequencies are characterized by very dynamic channel conditions which lead to wide fluctuations in the received signal quality. This article explains how the end-to-end user experience in mobile mmWave networks could be affected by a sub-optimal interaction between the most widely used transport protocol, TCP, and mmWave links. It also provides insights on the throughput-latency trade-off when Multipath TCP (MP-TCP) is used judiciously across various links (e.g., LTE and mmWave).

Research on the control strategies of data flow transmission paths for MPTCP-based communication networks.

The performance of multipath transmission control protocol (MPTCP) subflow through the enhancement mechanism of the MPTCP communication is improved. When dealing with multiple MPTCP subflows occupying the same transmission path, critical issues such as selection and optimization of multipath, and efficient scheduling of available multiple tracks are effectively addressed by incorporating the technology called software defined network (SDN) that is constructed based on four key parameters, namely, network transmission bandwidth, transmission paths, path capacity, and network latency. Besides, critical equipment such as the network physical device layer and SDN controller are integrated with the four parameters. So, the network model defines the transmission control process and data information. Considering the predetermined total network bandwidth capacity to select multiple paths, the adequate bandwidth capacity is determined by defining the data transfer rate between MPTCP terminals and MPTCP servers. However, the processing latency of the OpenFlow switch and the SDN controller is excluded. The effective network transmission paths are calculated through two rounds of path selection algorithms. Moreover, according to the demand capacity of the data transmission and the supply capacity of the required occupied network resource, a supply and demand strategy is formulated by considering the bandwidth capacity of the total network and invalid network latency factors. Then, the available network transmission path from the valid network transmission path is calculated. The shortest path calculation problem, which is the calculation and sorting of the shortest path, is transformed into a clustering, Inter-Cluster Average Classification (ICA), problem. The instruction of the OpenFlow communication flow is designed to schedule MPTCP subflows. Thus, various validation objectives, including the network model, effective network latency, effective transmission paths, supply-demand strategies, ineffective transmission paths, shortest feasible paths, and communication rules are addressed by the proposed method whose reliability, stability, and data transmission performance are validated through comparative analysis with other conventional algorithms. Found that the network latency is around 20 s, the network transmission rate is approximately 10Mbps, the network bandwidth capacity reaches around 25Mbps, the network resource utilization rate is about 75%, and the network swallowing volume is approximately 3M/s.

FlexCP: A Scalable Multipath TCP Proxy for Cellular Networks

Research has shown that Multipath TCP (MPTCP) improves the quality of a TCP connection by exploiting multiple paths, but its adoption in the wide area network is still fledgling. While MPTCP-TCP proxying is often employed as a practical solution, the performance of a split-connection proxy is suboptimal – it wastes CPU cycles on content relaying between two connections while it does not efficiently leverage multiple CPU cores in packet processing. We present FlexCP, a high-performance MPTCP-TCP proxy based on the following properties. First, FlexCP operates by translating the two protocols on a packet level. This approach not only avoids the overhead of flow reassembly and memory copying, but it greatly simplifies the implementation as the proxy stays away from reliable data transfer, socket buffer management, and per-hop congestion/flow control. Second, FlexCP maintains connection-to-core affinity for multiple subflows of the same MPTCP connection and its corresponding TCP connection by leveraging SmartNIC. This enables a lock-free implementation for packet processing, which significantly improves the performance. Our evaluation demonstrates that FlexCP achieves 281 Gbps of connection proxying on a single machine, outperforming existing proxies by up to 6.3× in terms of throughput while it incurs little extra latency over direct TCP/MPTCP connections.

An intelligent path management in heterogeneous vehicular networks

Achieving reliable connectivity in heterogeneous vehicular networks is a challenging task, owing to rapid topological changes and unpredictable vehicle speeds. As vehicular communication demands continue to evolve, multipath connectivity is emerging as an important tool, which promises to enhance network interoperability and reliability. Given the limited coverage area of serving access technologies, frequent disconnections are to be expected as the vehicle moves. To ensure seamless communication in dynamic vehicular environments, an intelligent path management algorithm for Multipath TCP (MPTCP) has been proposed. The algorithm utilizes a network selection mechanism based on Fuzzy Analytic Hierarchy Process (FAHP), which dynamically assigns the most appropriate underlying network for each running application. The selection process takes into account multiple factors, such as path quality, vehicle mobility, and service characteristics. In contrast to existing solutions, our proposed method offers a dynamic and comprehensive approach to network selection that is tailored to the specific needs of each service to ensure that it is always paired with the optimal access technology. The results of the evaluation demonstrate that the proposed method is highly effective in maintaining service continuity during vertical handover. By tailoring the network selection to the specific needs of each application, our path manager is able to ensure optimal connectivity and performance, even in challenging vehicular environments, delivering a better user experience, with more reliable connections, and smoother data transfers.

Energy-Aware MPTCP Scheduling in Heterogeneous Wireless Networks Using Multi-Agent Deep Reinforcement Learning Techniques

This paper proposes an energy-efficient scheduling scheme for multi-path TCP (MPTCP) in heterogeneous wireless networks, aiming to minimize energy consumption while ensuring low latency and high throughput. Each MPTCP sub-flow is controlled by an agent that cooperates with other agents using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. This approach enables the agents to learn decentralized policies through centralized training and decentralized execution. The scheduling problem is modeled as a multi-agent decision-making task. The proposed energy-efficient scheduling scheme, referred to as EE-MADDPG, demonstrates significant energy savings while maintaining lower latency and higher throughput compared to other state-of-the-art scheduling techniques. By adopting a multi-agent deep reinforcement learning approach, the agents can learn efficient scheduling policies that optimize various performance metrics in heterogeneous wireless networks.

A faster failure recovery mechanism for Multipath TCP

A faster failure recovery mechanism for Multipath TCP

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.

Learning-Based Real-Time Transmission Control for Multi-Path TCP Networks

The evolution of the Internet of Things and 5G/6G networks is ushered in the exponential proliferation of mobile devices with multiple wireless interfaces. Multi-Path TCP (MPTCP) is a widely used protocol that provides reliable connections with multiple parallel paths for these devices. To cope with the increasingly complex multi-path network environments, some learning-based methods have been proposed to achieve better control than existing heuristic methods in congestion control and multi-path scheduling. However, most learning-based efforts use Deep Reinforcement Learning (DRL) requiring the agent and the environment to evolve alternatively, which cannot adapt to the constantly active MPTCP networks. In this paper, we propose Real-time Multi-path Transmission Control (RMTC), a cognitive framework that achieves real-time congestion control and multi-path scheduling in MPTCP networks, allowing the agent and the environment to evolve simultaneously. It adopts the Multi-Agent DRL (MADRL) to improve the granularity of the control and handle the change in the number of sub-flows. Besides, RMTC develops a pacing mechanism in MPTCP connections for all congestion control algorithms to alleviate network fluctuations, which helps the agent make accurate decisions. Extensive experimental results show that RMTC obtains better network performance than existing learning-based or heuristic methods and considers TCP-friendliness and the Head-of-Line (HoL) blocking problems.

EdAR: An Experience-Driven Multipath Scheduler for Seamless Handoff in Mobile Networks

Multipath TCP (MPTCP) improves the bandwidth utilization in wireless network scenarios, since it can simultaneously utilize multiple interfaces for data transmission. However, with the fast growth of mobile devices and applications, link interruptions caused by handoffs still lead to drastic performance degradation in such scenarios. Typically, a series of packet losses on part of the links will block the transmission of the entire connection when handoff occurs. This paper proposes an Experience-driven Adaptive Redundant packet scheduler (EdAR) for MPTCP, aiming at achieving seamless handoffs in mobile networks. EdAR enables flexibly scheduling redundant packets with an experience-driven learning-based approach in the face of drastic network environment changes for multipath performance enhancement. To enable accurate learning and prediction, both the network environment and the best course of actions are jointly learned via a Deep Reinforcement Learning (DRL) agent, which we design with a hybrid structure to deal with the complexity of system states. Furthermore, both offline and online learning are utilized to allow the agent to adapt to different and changing network environments. Evaluation results show that EdAR outperforms the state-of-the-art MPTCP schedulers in most network scenarios. Specifically in mobile networks with frequent handoffs, EdAR brings 2× improvement in terms of the overall goodput.

Learning agent-based security schema mitigating man-in-the-middle attacks in fog computing

<div style="text-align: start;" align="center"><span lang="EN-US">The fast emerging of internet of things (IoTs) has introduced fog computing as an intermediate layer between end-users and the cloud datacenters. Fog computing layer characterized by its closeness to end users for service provisioning than the cloud. However, security challenges are still a big concern in fog and cloud computing paradigms as well. In fog computing, one of the most destructive attacks is man-in-the-middle (MitM). Moreover, MitM attacks are hard to be detected since they performed passively on the network level. This paper proposes a MitM mitigation scheme in fog computing architecture. The proposal mapped the fog layer on software-defined network (SDN) architecture. The proposal integrated multi-path transmission control protocol (MPTCP), moving target defense (MTD) technique, and reinforcement learning agent (RL) in one framework that contributed significantly to improving the fog layer resources utilization and security. The proposed schema hardens the network reconnaissance and discovery, thus improved the network security against MitM attack. The evaluation framework was tested using a simulation environment on mininet, with the utilization of MPTCP kernel and Ryu SDN controller. The experimental results shows that the proposed schema maintained the network resiliency, improves resource utilization without adding significant overheads compared to the traditional transmission control protocol (TCP).</span></div>

An Online Learning Assisted Packet Scheduler for MPTCP in Mobile Networks

Multipath TCP is designed to utilize multiple network paths to achieve improved throughput and robustness against network failure. These features are supposed to make MPTCP preferable to single-path TCP in mobile networks. However, it fails to achieve the expected performance in practice. A key challenge of using MPTCP in mobile networks is how to effectively spread packets over heterogeneous and unstable network paths to mobile devices with limited buffers. If packets are not sent in an effective way, MPTCP may only provide equal or even lower throughput than single-path TCP. Several packet scheduling algorithms have been designed to tackle this challenge. Unfortunately, they still cannot achieve the expected performance in dynamic scenarios such as mobile networks. In this paper, we propose an Online-Learning Assisted Packet Scheduler (OLAPS) to solve the packet scheduling problem by modeling it as a multi-armed bandit problem. Over time, OLAPS can adaptively learn from current network conditions to make the best scheduling policy to provide the highest possible throughput in a dynamic environment. Moreover, when the inbuilt reward monitor detects the mismatch between network conditions and the learned policy, OLAPS aborts the outdated policy and switches to a new one swiftly. We implement OLAPS as a Linux kernel module and evaluate it over a wide range of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ns-3</i> -simulated network conditions. The results show that OLAPS retains MPTCP’s ability to provide higher throughput and also significantly improves the throughput performance of MPTCP when other in-kernel schedulers suffer a dramatic throughput decline.

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.