MPTCP Throughput Enhancement by Q-Learning for Mobile Devices

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.

With increasing deployment of Multipath TCP (MPTCP) in multihoming and data enter scenarios, there is a need to understand how its performance is affected in practice-both by traditional factors such as RTT measurements, and by new multipath-specific considerations such as sub flow selection. We carried out an initial but comprehensive study using an actual MPTCP implementation in an emulated network environment, to explore the impact of different factors on MPTCP throughput. We find that path selection and packet scheduling have a large effect on performance, and that merely trusting the congestion control mechanism to do the right thing is not enough. Moreover, we provide evidence that throughput can be improved by slight modifications to the send buffers and path selection components of the implementation. Important challenges in network design remain, if only to ensure that multiple suitable paths exist in a network.

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.

Enhancing MPTCP performance in wireless networks with PRS-MPTCP

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.

Multipath TCP (MPTCP) is a promising technique for boosting application throughput while using well-known and versatile network socket interfaces. Recently, many interesting applications of MPTCP in various environments such as wireless networks and data centers have been proposed, but little work has been done to investigate the impact of this protocol on conventional network devices. For example, MPTCP throughput advantage can be better achieved if all MPTCP subflows are routed on disjoint paths, but this is currently not feasible since routers are not designed to recognize the membership of MPTCP subflows. In this paper, we take a first step to address this issue by proposing SAMPO, an online algorithm to detect and associate MPTCP subflows in network. The main challenge is that sampling techniques and network dynamics may cause a network device to only obtain partial flow records. SAMPO takes advantage of both protocol information and statistical characteristics of MPTCP data sequence number to overcome the challenge in network. Through analysis and experimentation, we show that SAMPO is able to detect and associate MPTCP subflows with high accuracy even when a small portion of the entire flow records are available.

Modern computing devices such as smartphones and personal computers provide multiple network interfaces such as WiFi and 3G/LTE cellular network. Multipath TCP (MPTCP) is a commonly proposed method of aggregating the bandwidth of these interfaces. However, how MPTCP affects the quality of experience of existing services, especially to variances in bandwidth and latency among the individual paths over the wireless networks is not well-known. In this work, we explore the quality of service (QoS) and quality of experience (QoE) of adaptive video streaming using MPTCP over wireless network given its vast popularity and significant bandwidth demand. Unlike prior works, we conduct systematic measurements over three mobile network operators, AT&T, Verizon Wireless (VzW), and T-Mobile, along with WiFi. Based on extensive measurements, we show that MPTCP can improve the QoS and QoE of video streaming only if the network interfaces have the roughly similar bandwidth and latency. Our studies also show that MPTCP can perform worse than TCP in case of extreme differences between the network interfaces.

TCP was originally designed for single session, thus could not utilize resources of multiple paths. MPTCP (Multipath TCP) provides enhanced performance and reliability by using multiple paths simultaneously at the Transport layer. But, LIA based congestion control of basic MPTCP does not show good performance when characteristics of two path are asymmetrical in the aspects of bandwidth in high-speed networks. In this paper, we suggest new MPTCP control scheme by applying TCP CUBIC on the high-speed path and modified LIA on the other low-speed data path. Our proposed scheme can enhance performance of high-speed asymmetric path more than 4 times compared to the basic MPTCP protocol.

One of the main goals of multipath TCP (MPTCP) is to achieve higher throughput than regular TCP by utilizing multiple paths simultaneously. When these paths share a common bottleneck, MPTCP tries not to be more aggressive than a regular TCP flow. This is achieved by MPTCP's coupled congestion control mechanism that couples the increase factor of MPTCP's subflows in congestion avoidance. However, slow-start remains unchanged and behaves uncoupled for each subflow, affecting MPTCP and concurrent traffic at the bottleneck. We propose LISA, a simple algorithm for coupling MPTCP subflows in slow-start, and investigate the trade-off that this coupling entails. Our evaluations show that coupling in slow-start not only provides gains for MPTCP but also for a concurrent TCP at the bottleneck.

Recent data centers provide dense inter-connectivity between each pair of servers through multiple paths. These data centers offer high aggregate bandwidth and robustness by using multiple paths simultaneously. Multipath TCP (MPTCP) protocol is developed for improving throughput, fairly sharing network link capacity and providing robustness during path failure by utilizing multiple paths over multi-homed data center networks. Running MPTCP protocol for latency-sensitive rack-local short flows with many-to-one communication pattern at the access layer of multi-homed data center networks creates MPTCP incast problem. In this paper, Balanced Multipath TCP (BMPTCP) protocol is proposed to mitigate MPTCP incast problem in multi-homed data center networks. BMPTCP is a window-based congestion control protocol that prevents constant growth of each worker’s subflow congestion window size. BMPTCP computes identical congestion window size for all concurrent subflows by considering bottleneck Top of Rack (ToR) switch buffer size and increasing count of concurrently transmitting workers. This helps BMPTCP to avoid timeout events due to full window loss at ToR switch. Based on current congestion situation at ToR switches, BMPTCP adjust transmission rates of each worker’s subflow so that total amount of data transmitted by all concurrent subflows does not overflow bottleneck ToR switch buffer. Simulation results show that BMPTCP effectively alleviates MPTCP incast. It improves goodput, reduces flow completion time as compared to existing MPTCP and EW-MPTCP protocols.

With the growing demand of Internet services, network operators have put significant efforts to improve network error resilience and efficiency. Since there exist different wired/wireless technologies for Internet access such as digital subscriber line (DSL), Ethernet, and worldwide interoperability for microwave access (WiMax), a mobile host can use multiple access networks simultaneously with multipath transmission. Taking the advantage of heterogeneous environment, multipath transmission through the Internet can improve service reliability and network flexibility. Ensuring a reliable end-to-end connection oriented communication with satisfactory quality of service (QoS) and maintaining congestion control and flow control are the main responsibilities of the Transmission Control Protocol (TCP), the dominant transport layer protocol in the Internet. In this paper, we survey the state-of-the-art of multipath transmission techniques for QoS provisioning in wireless Internet access, focusing on the end-to-end transport layer protocols. The main challenges for the design of multipath TCP are reviewed, and the existing transport layer congestion control schemes are categorized. Multipath TCP and stream control transmission protocol (SCTP)-based transport layer protocols are discussed, and their limitations and/or impractical assumptions are addressed. Open research issues on the development of an effective, efficient, and practical multipath TCP protocol are summarized.

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.

Congestion is a major factor which affects the data center network traffic. Major applications of data center producing huge data traffic are social networks, online shopping portals, online banking portals, mobile application data, weather forecasting applications, Internet of Things applications and so on. Handling such large amount of data is a difficult task for data center architectures and mechanisms and always lead to congestion. The congestion control mechanisms already proposed in existing research works for Software Defined Data Centers lack behind in terms of scalability and throughput due to increase in data center traffic and centralized controller environment. So, in this paper, we are proposing a Simple Congestion Avoidance mechanism for Multipath Transmission Control Protocol traffic using Random Early Detection Algorithm to overcome the limitation of huge packet loss for a specific flow and detection of congested links in the early stage. In our proposed work, path generation among the particular source and destination has been carried out as Multipath Transmission Control Protocol traffic which can prevent congestion state due to the generation of sub flows between two hosts. Then, we bring up Random Early Detection mechanism as a congestion avoidance strategy to minimize overloaded links, link delay and to enhance throughput during packet transmission. This implementation is carried out in the network simulator, Mininet with OpenDaylight controller setup which runs on the top of Jellyfish data center topology. We finally compare the performance of proposed work with an existing congestion avoidance mechanism and show that our implementation offers better throughput by considering the link load, capacity for different node ranges.

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.

Resource Pooling concept has been proposed as fair sharing of total network resources among all users sharing a network so that a whole network is treated as a single pooled resource. MPTCP (Multipath TCP) has been proposed as one of the most promising congestion controls which realize the Resource Pooling in the situation that each user can utilize multiple paths. In CCN (Content-Centric Networking), several multipath/multisource congestion control methods have been proposed. A fundamental difference between CCN congestion control and MPTCP where single source is assumed, is multisource and the Resource Pooling in multipath/multisource situation is quite a new research issue. In this paper, we discuss congestion control for CCN from the viewpoint of the Resource Pooling and show that our previously proposed congestion control satisfies the Resource Pooling concept. Our simulation results also show that our proposed congestion control achieves macroscopic fairness among users, which means the Resource Pooling concept is satisfied.