Multipath TCP (MPTCP) has gained popularity to enhance data transmission. From the last decade, proposed MPTCP enhancement schemes for congestion control, path management, and packet scheduling, have been used to benefit transmission performance. However, despite their efforts, they are exigent with a comprehensive understanding of real-world performance to guide the implementation of MPTCP to a more complex wireless network. To that end, we conduct a measurement-driven study of MPTCP enhancement schemes, providing insights and in-depth demonstrations of their performance with a comprehensive real-world platform. Our finding indicates that the enhancement schemes struggle to consistently maintain high performance at all times. One can achieve optimal efficiency in its specific scenarios, but suffers extreme degradation at times. To eliminate this transmission uncertainty in wireless networks, we further propose an adaptive control framework OLSch to integrate different schemes, emphasizing their strengths to provide consistently high performance. To be specific, OLSch is implemented with different scheduling schemes and leverages an online-learning-driven approach to choose one that best fits the current network conditions. Evaluations show that OLSch obviously improves the stability of transmission in harsh network scenarios, eliminates performance degradation, and increases the 95% tail throughput by 1.45×-2.39×.

The utilization of Multi-path TCP (MPTCP) has been demonstrated to provide superior transport-layer support for data center networks (DCNs) due to its exceptional resource utilization and load-balancing capabilities. However, the substantial path diversity can make it challenging to utilize network resources to their full potential in DCNs. This paper focuses on studying the resource allocation issue of MPTCP from a resource optimization perspective. Based on theoretical analysis, we propose FMPTCP, which uses a feedback-based congestion control algorithm (FCC) and a feedback-based multi-path routing algorithm (FMP) to jointly achieve high bandwidth utilization and low round-trip time (RTT) in DCNs. The FCC algorithm utilizes probabilistic explicit congestion notification (ECN) to provide feedback on path congestion degree, and uses a gradient descent method to adjust the congestion window for optimal resource utilization and load balancing under a fixed routing topology. On the other hand, the FMP algorithm employs a hop-by-hop feedback mechanism to notify in-network congestion and path delay information, allowing for transparent multi-path routing for MPTCP flows. Our extensive simulations demonstrate that FMPTCP enables effective network resource utilization, which not only enhances overall throughput but also reduces transmission latency for DCNs.

Existing congestion control algorithms for MPTCP that care about only long flow transmission aim at the Congestion-Avoidance (CA) phase and they need a long time to reach convergence states. We verified that the exponential growth of congestion window (cwnd) in the uncoupled Slow-Start (SS) leads to not only unfairness to TCP but also buffer overflow due to burst data. Moreover, these algorithms cannot support fair bandwidth sharing among TCP/MPTCP flows before reaching convergence at the bottleneck, which may reduce the transmission efficiency of short flows and even hurts long flows. In this paper, we propose a Throughput Consistency Congestion Control (TCCC) algorithm consisting of Coupled Slow-Start (CSS) and Aggressive Congestion Avoidance (ACA). To prevent packet loss caused by excessive burst data, CSS couples the increment of subflows’ cwnd and reset the ssthresh value to safely move the flows to CA when it achieves expected throughput. Based on CSS, ACA periodically detects path states and allocates the same throughput increment as the best TCP to subflows to achieve fair bandwidth share in CA. Finally, we implement TCCC in both NS3 and real testbed. The results show that TCCC reduces retransmissions, improves transmission efficiency, and maintains better fairness.

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 ns-3 -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.

A QUIC (Quick UDP Internet Connections) protocol is designed to improve Hypertext Transfer Protocol (HTTP) traffic and carries a non-negligible portion of the traffic in the current Internet. As its extension, Multipath QUIC (MPQUIC) provides higher bandwidth and smoother network handover by using multiple network interfaces simultaneously. However, to improve HTTP traffic, there are still some issues not yet carefully addressed in the existing MPQUIC, and packet scheduling is a vital one among the issues. Specifically, existing methods fail to respond to the stream prioritization of HTTP Version 2 (HTTP/2), leading to unsatisfying web page load performance. Besides, managing asymmetric and dynamic network paths is also a challenging issue, which may result in Head-of-Line (HoL) blocking and excessive buffer usage if not effectively handled. In this paper, we present a stream-aware per-packet scheduler, HoL Blocking Eliminating Scheduler (HBES), to improve the performance of MPQUIC in mobile networks. Firstly, HBES provides a fair allocation of aggregated bandwidth for different streams based on their priority. Then, it keeps stream data arriving at the receiver in order by estimating packet arrival time to mitigate HoL blocking and excessive buffer usage. We implement HBES and evaluate its performance in various network scenarios. Experimental results verify the superiority of HBES in reducing stream completion time and buffer occupation over those existing MPQUIC schedulers.

Many applications are bandwidth consuming but may tolerate longer flow completion times. Multipath protocols, such as multipath TCP (MPTCP), can offer bandwidth aggregation and resilience to link failures for such applications, and low priority congestion control (LPCC) mechanisms can make these applications yield to other time-sensitive ones. Properly combining the above two can improve the overall user experience. However, the existing LPCC mechanisms are not adequate for MPTCP. They do not take into account the characteristics of multiple network paths, and cannot ensure fairness among the same priority flows. Therefore, we propose a multipath LPCC mechanism, i.e., Dynamic Coupled Low Extra Delay Background Transport, named DC-LEDBAT. Our scheme is designed based on a standardized LPCC mechanism LEDBAT. To avoid unfairness among the same priority flows, DC-LEDBAT trades little throughput for precisely measuring the minimum delay. Moreover, to be friendly to single-path LEDBAT, our scheme leverages the correlation of the queuing delay to detect whether multiple paths go through a shared bottleneck. Then, DC-LEDBAT couples the congestion window at shared bottlenecks to control the sending rate. We implement DC-LEDBAT in a Linux kernel and experimental results show that DC-LEDBAT can not only utilize the excess bandwidth of MPTCP but also ensure fairness among the same priority flows.

360-degree virtual reality videos greatly improve the video experience by providing users with a more immersive and interactive environment than standard streaming video. However, 360-degree videos suffer from bandwidth limits. Existing bandwidth-efficient solutions mainly focus on spatially cutting 360-degree video into tiles, and only provide video content in the Field-of-View (FoV) of users with high quality to reduce bandwidth consumption. Although existing tile-based schemes can reduce the bandwidth consumption, the bandwidth and transmission delay provided by a single-path TCP may still not meet the high requirements of 360-degree videos. Multipath TCP (MPTCP) allows a TCP connection to operate across multiple paths simultaneously and becomes highly attractive to support the mobile devices with various radio interfaces to aggregate multipath bandwidth and improve the throughput. In this paper, by taking the advantage of MPTCP, we propose an MPTCP-based adaptive streaming framework for 360-degree Virtual Reality videos, named MP-VR. MP-VR dynamically selects the appropriate tile bitrate according to the bandwidth and transmission delay of different subflows. Then it schedules the video segments to subflows to improve QoE of users. We conduct experiments on a testbed in our lab and simulations on NS-3. Evaluation results show that MP-VR outperforms existing tile-based strategies when network fluctuations or errors in FoV predictions occur.

As the user demand for data transmission over high-speed and long distance (hereafter abbreviated as HSLD) networks increases significantly, multipath TCP (MPTCP) shows a great potential to further improve the utilization of HSLD network resources than traditional TCP, and provides better quality of service (QoS). It has been reported that TCP causes serious waste of bandwidth in HSLD networks, while MPTCP can transmit data by using multiple network paths simultaneously between two distant hosts, thus provides better resource utilization, higher throughput and smoother failure recovery for applications. However, the existing multipath congestion control algorithms cannot perfectly meet the efficiency requirements of HSLD network, since they mainly emphasize fairness rather than other critical indicators of QoS such as throughput, but still encounter fairness issues when coexist with various TCP variants. To solve these problems, we develop weighted Compound (wCompound), a loss-and-delay-based compound multipath congestion control algorithm which is originated from Compound TCP, and is applicable to HSLD networks. Different from the traditional methods of setting an empirical value as the threshold, wCompound innovatively adopts a dynamic threshold and have the flexibility to adjust the sending window of each subflow based on current network state, so as to effectively couple all subflows and fully utilize the network capacity. Moreover, with the cooperation of delay-based and loss-based methods, wCompound also ensures good fairness to different types of TCP variants. We implement wCompound in the Linux kernel, then carry out sufficient experiments on our testbed. The results show that wCompound achieves higher utilization of network resources and can always maintain an appropriate throughput no matter competing with loss-based or delay-based network traffic.

Multipath TCP (MPTCP) utilizes multiple paths for simultaneous data transmission to enhance performance. However, existing MPTCP protocols are still far from satisfactory in wireless networks because of their loss-based congestion control and the difficulty of managing multiple subflows. To overcome these problems, we redesign the coupled congestion control algorithm and scheduler to boost MPTCP in wireless heterogeneous networks. The main purpose is to promote transmission rate under lossy networks, while also provide stability when networks suffer physical link changes and asymmetric links. In this paper, inspired by Bottleneck Bandwidth and Round-trip propagation time (BBR), we first propose Coupled BBR that utilizes detected bandwidth to adjust the sending rate within an MPTCP connection. Coupled BBR provides high loss tolerance as well as balanced congestion among MPTCP subflows. Then, to further improve the performance, we propose an Adaptively Redundant and Predictive packet (AR&P) scheduler to improve adaptability and keep in-order packet delivery in highly dynamic network scenarios. Based on Linux kernel implementation and experiments in both testbed and real network scenarios, we show that the proposed scheme not only provides high throughput in wireless networks, but also improves robustness and reduces out-of-order packets in some harsh circumstances.

It is a known issue that low-latency communication is hard to achieve when using multiple network interfaces with asymmetric capacity and delay (e.g., LTE and WLAN) simultaneously. A main underlying cause of this issue is that the packets with lower sequence number are stalled on a high-latency path, thus the early arriving packets with higher sequence number become “out-of-order (OFO)” packets. These OFO packets may excessively consume receiver’s buffer, causing long reordering delay and unnecessary packet retransmission. In this paper, we present a novel design of packet scheduling for Multipath TCP (MPTCP), called OverLapped Scheduler (OLS), able to tackle the OFO-packet problem more effectively. OLS can guarantee sufficient throughput on demand of upper layer applications, and utilizes the remaining bandwidth to reduce OFO-packets. To do so, OLS schedules packets according to their arrival time and sends a controlled number of redundant packets to avoid the impact of inaccurate arrival-time estimations due to network jitter. We implement OLS in a Linux kernel, and the experiments show that in asymmetric networks with or without jitter, OLS can effectively reduce OFO-packets and transmission latency while maintaining a sufficient throughput, which makes it fully capable to meet the requirements of applications such as live video streaming.

Named data networking (NDN) is an emerging network paradigm that decouples content from its storage location by providing one or more content copies and distributing them within the whole network. Congestion control is a fundamental and important problem in NDN, but it has not been well solved yet. Existing works can be divided into three main types, receiver driven flow based control, hop-by-hop interest shaping and hybrid control. While they are faced with more or less high computational complexity, multi-content source and multitransmission path problems, we proposed our edge computing aided congestion control scheme (EACC). The main idea is to detect congestion along the transmission path and avoid it by interest forwarding control at edge nodes. We add a new field to data packet to record the congestion status of the transmission path when it returns. After that, we deploy the core computing functions of the solution at edge nodes, and formulate the interest packet forwarding control into a local MDP (Markov Decision Process) problem based on the returned path congestion status and local user request information. Then we use neuro-dynamic programming (NDP) to solve this decision problem and present a practical implementation at edge nodes. The proposed scheme is implemented in ndnSIM simulator and compared to other two methods. Simulation results show the effectiveness of our scheme.

Nowadays, mobile devices are equipped with multiple interfaces connected to various networks, which makes it possible to aggregate bandwidth in actual application. Multipath TCP (MPTCP) is one of the transport protocols that uses multiple interfaces simultaneously and provides robust and efficient data transmission. In practice, MPTCP will interact with various network facilities. Among them, Content Delivery Network (CDN) is a popular one, which is a widely distributed network system deployed across the Internet. Using MPTCP in CDN could provide better performance for users, however, we find that CDN may not give full play to its functions when working with MPTCP. Because the Default Server Selection (DSS) mechanism in CDN only obtains servers optimal in single path connection scenarios, it may not provide the globally optimal server for MPTCP. In this paper, we propose a new algorithm called Server Selection for MPTCP (SSMP), which utilizes all available multi-homed sources to provide the globally optimal performance. SSMP modifies the DNS mechanism to return the optimal server for each available interface by the origin strategy and further selects the globally optimal server for both elephant and mice flows. We compare SSMP with DSS through experiments under video streaming and file download scenarios with both stable and variable environments. Our results show that SSMP consistently utilizes available paths more efficiently than DSS, particularly for servers with a great gap in server quality.

The whole Software-Defined Networking (SDN) system might be out of service when the control plane is overloaded by control plane saturation attacks. In this attack, a malicious host can manipulate massive table-miss packets to exhaust the control plane resources. Even though many studies have focused on this problem, systems still suffer from more influenced switches because of centralized mitigation policies, and long recovery delay because of the remaining attack flows. To solve these problems, we propose FSDM, a Fast recovery Saturation attack Detection and Mitigation framework. For detection, FSDM extracts the distribution of Control Channel Occupation Rate (CCOR) to detect the attack and locates the port that attackers come from. For mitigation, with the attacker's location and distributed Mitigation Agents, FSDM adopts different policies to migrate or block attack flows, which influences fewer switches and protects the control plane from resource exhaustion. Besides, to reduce the system recovery delay, FSDM equips a novel functional module called Force_Checking, which enables the whole system to quickly clean up the remaining attack flows and recovery faster. Finally, we conducted extensive experiments, which show that, with the increasing of attack PPS (Packets Per Second), FSDM only suffers a minor recovery delay increase. Compared with traditional methods without cleaning up remaining flows, FSDM saves more than 81% of ping RTT under attack rate ranged from 1000 to 4000 PPS, and successfully reduced the delay of 87% of HTTP requests time under large attack rate ranged from 5000 to 30000 PPS.