Design, implementation and evaluation of congestion control for multipath TCP

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.

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.

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.

A single flow stream could be divided into several sub-flows to be sent across multiple paths. This method obtains obvious advantages against traditional TCP as it maintains higher reliability and makes bettaer use of the network resources. Congestion control algorithm is an essential part to be considered. This paper first reviews the existing multipath TCP congestion control algorithms and then analyzes and formulates the goals and problems need to be achieved and solved. A delay-based congestion control algorithm named Weighted Vegas (wVegas) is provided. Finally, two possible modifications are demonstrated including adjusting the congestion control window according to how far the path is from congestion and redefining the behavior when loss occurs.

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.

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.

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.

Satellite systems might become an important supplement to 5G systems. In addition, compared with regular TCP, multipath TCP (MPTCP) provides a robust solution. However, due to the high latency and high loss of satellite networks, most of the existing multipath TCP congestion control schemes do not perform well. Therefore, we proposed a bandwidth and round-trip time detection-based congestion control (BWRD) for multipath TCP over highly lossy satellite networks. Simulations are running to evaluate the proposed BWRD algorithm. It is shown that compared to previous solutions, our strategy significantly improves throughput performance and guarantees the fairness among the various sub-flows.

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.

Today's laptops and mobile terminals are equipped with several network interfaces belonging to different access technologies. Users can benefit from multi-homing to access network services anywhere, at any time and from any network. Multipath TCP (MPTCP) is a modified version of Transmission Control Protocol (TCP) which allows a TCP connection to simultaneously send data over multiple paths. One of the objectives of MPTCP is to improve the throughput of TCP connection. A MPTCP connection over multiple paths should achieve a no worse throughput than a single TCP connection over the best constituent path. In this paper, we evaluate the performance of MPTCP in terms of throughput optimization and load sharing. The measurements show that the context of heterogeneous networks (Ethernet, Wifi and 3G) has a great impact on the MPTCP performances and reveal the need of an intelligent algorithm for interface selection in MPTCP.

Today, there is a generalized standard usage of internet for all.The devices via multiple technologies that facilitates to provide few communication methods to scholars to work with. By forming multiple paths in the data center network, latest generation data centers offer maximum bandwidth with robustness. To utilize this bandwidth, it is necessary that different data flows take separate paths. In brief, a single-path transport seems inappropriate for such networks. By using Multipath TCP, we must reconsider data center networks, with a diverse approach as to the association between topology, transport protocols, routing. Multipath TCP allows certain topologies that single path TCP cannot use. In newer generation data centers, Multipath TCP is already deployable using extensively deployed technologies such as Equal-cost multipath routing. But, major benefits will come when data centers are specifically designed for multipath transports. Due to manifold of technologies like Cloud computing, social networking, and information networks there is a need to deploy the number of large data centers. While Transport Control Protocol is the leading Layer-3 transport protocol in data center networks, the operating conditions like high bandwidth, small-buffered switches, and traffic patterns causes poor performance of TCP. Data Center TCP algorithm has newly been anticipated as a TCP option for data centers which address these limitations. It is worth noting that traditional TCP protocol.

Mobile devices are able to leverage diverse heterogeneous network paths by Multi-Path Transmission Control Protocol (MPTCP); nevertheless, boosting MPTCP throughput in wireless networks is a real bear. Not only the best path(s) should be selected, but also the optimal congestion control mechanism should be chosen. We investigate the impact of different paths and congestion control for different signal quality states. Consequently, we present the novel MPTCP algorithm augmenting the end user throughput by understating the best policy in different situations by Q-learning. The Results reveal a tremendous effect of switching between the different interfaces and changing the congestion control mechanism on throughput and delay. By and large, the proposed framework achieves 10% more throughput compared to base MPTCP.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Multimedia streaming over the Internet has been a very challenging issue due to the dynamic uncertain nature of the channels. This paper proposes an algorithm for the joint design of source rate control and congestion control for video streaming over the Internet. With the incorporation of a virtual network buffer management mechanism (VB), the quality of service (QoS) requirements of the application can be translated into the constraints of the source rate and the sending rate. Then at the application layer, the source rate control is implemented based on the derived constraints, and at the transport layer, a <emphasis emphasistype="italic">QoS-aware</emphasis> congestion control mechanism is proposed that strives to meet the send rate constraint derived from VB, by allowing temporary violation of transport control protocol (TCP)-friendliness when necessary. Long-term TCP-friendliness, nevertheless, is preserved by introducing a rate-compensation algorithm. Simulation results show that compared with traditional source rate/congestion control algorithms, this cross-layer design approach can better support the QoS requirements of the application, and significantly improve the playback quality by reducing the overflow and underflow of the decoder buffer, and improving quality smoothness, while maintaining good long-term TCP-friendliness. </para>

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.

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.