MPTCP fairness algorithm based on DTW and BBR

Multipath TCP (MPTCP) is a backward compatible extension of Transmission Control Protocol (TCP) designed to let an application send data over several TCP connections, usually called subflows. MPTCP presents several opportunities over legacy TCP, especially when these subflows follow disjoint paths: for instance bandwidth aggregation or seamless mobility across networks. While MPTCP is built as an extension of TCP, the scope of the modifications qualifies it almost as a new protocol. This means traditional TCP tools are not enough to analyze MPTCP performance. We thus develop and present an open source application able to analyze MPTCP specific metrics such as the aggregated goodput or inter-subflow retransmissions.

<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>

A significant enhancement in recent communication systems is Satellite Communications. The performance improvement of the transmission control protocol (TCP) in satellite communications has been an active research area. Multipath TCP (MPTCP) ensures a robust solution than the regular TCP. However, the performance degradation in satellite networks due to the link asymmetry, high loss and high latency cannot distinguish the packet losses caused by network congestions from the losses properly. In this paper, a round-trip time detection-based algorithm is proposed using available bandwidth estimation (ABE) to mitigate the issue of blind decreasing of congestion window upon detecting the packet loss over lossy satellite networks. Experiments are done using the NS-2 network simulator to compare the various congestion control algorithms of both TCP and MPTCP over satellite networks. Simulations results from the experiments show that our proposed algorithm outperforms the existing algorithms which are mentioned in this article.

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 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.

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.

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.

Classical Transmission Control Protocol (TCP) designs have never considered the identity of the competing transport protocol as useful information to TCP sources in congestion control mechanisms. When competing against a TCP flow on a bottleneck link, a User Datagram Protocol (UDP) flow can unfairly occupy the entire link bandwidth and suffocate all TCP flows on the link. If it were possible for a TCP source to know the type of transport protocol that deprives it of link access, perhaps it would be better for the TCP source to react in a way which prevents total starvation. In this paper, we use coefficient of variation and power spectral density of throughput traces to identify the presence of UDP transport protocols that compete against TCP flows on bottleneck links. Our results show clear traits that differentiate the presence of competing UDP flows from TCP flows independent of round-trip times variations. Signatures that we identified include an increase in coefficient of variation whenever a competing UDP flow joins the bottleneck link for the first time, noisy spectral density representation of a TCP flow when competing against a UDP flow in the bottleneck link, and a dominant frequency with outstanding power in the presence of TCP competition only. In addition, the results show that signatures for congestion caused by competing UDP flows are different from signatures due to congestion caused by competing TCP flows regardless of their round-trip times. The results in this paper present the first steps towards development of more ’intelligent’ congestion control algorithms with added capability of knowing the identity of aggressor protocols against TCP, and subsequently using this additional information for rate control.

Cloud computing data services have extremely high requirements for transmission performance in high-concurrency and heterogeneous network environments, while traditional single-path transmission mechanisms are difficult to fully utilize the multi-link resources in the cloud-edge collaborative architecture. This paper proposes a deep reinforcement learning (DRL) strategy for cloud-edge multi-path transmission in cloud computing data services. By constructing a state-action-reward model, it realizes dynamic perception and decision optimization of link bandwidth, latency, packet loss rate, and congestion level. The method includes: (1) establishing a cloud-edge collaborative multi-link environment model and performing real-time sampling and normalization of link feature parameters; (2) designing an agent based on a double deep Q-network (DDQN) to learn the policy of transmission path and data allocation ratio; (3) improving learning efficiency through a prioritized experience replay mechanism; and (4) combining an adaptive reward function to achieve a balance between throughput and latency in the system. Experiments were conducted in simulated and real-world deployment environments. Compared with the traditional Multipath Transmission Control Protocol (MPTCP) and the optimization scheme without reinforcement learning, the proposed method improves the average throughput by about 28.3% and reduces the end-to-end latency by about 15.4% compared with MPTCP, while achieving a link utilization of 92%, demonstrating superior dynamic decision-making and self-optimization capabilities.

The differentiated services (DiffServ) is proposed to provide packet level service differentiations in a scalable manner. To provide an end-to-end service differentiation to users having a connection over multiple domains, as well as a flow marker, an intermediate marker is necessary at the edge routers, and it should not be operated at a flow level due to a scalability problem. Due to this operation requirement, the intermediate marker has a fairness problem among the transmission control protocol (TCP) flows since TCP flows have intrinsically unfair throughputs due to the TCP's congestion control algorithm. Moreover, it is very difficult to resolve this problem without individual flow state information such as round trip time (RTT) and sending rate of each flow. In this paper, to resolve this TCP fairness problem of an intermediate marker, we propose a fair scalable marker (FSM) as an intermediate marker which works with a source flow three color marker (sf-TCM) operating as a host source marker. The proposed fair scalable marker improves the fairness among the TCP flows with different RTTs without per-flow management. Through the simulations, we show that the FSM can improve TCP fairness as well as link utilization in multiple domain DiffServ networks.

To alleviate the lower performance of Transmission Control Protocol (TCP) congestion control over complex network, especially the high latency and packet loss scenario, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm. In contrast with other TCP congestion control algorithms, BBR adjusted transfer data by maximizing delivery rate and minimizing delay. However, some evaluation experiments have shown that the persistent queues formation and retransmissions in the bottleneck can lead to serious fairness issues between BBR flows with different round-trip times (RTTs). They pointed out that small RTT differences cause unfairness in the throughput of BBR flows and flows with longer RTT can obtain higher bandwidth when competing with the shorter RTT flows. In order to solve this fairness problem, an adaptive congestion window of BBR is proposed, which adjusts the congestion window gain of each BBR flow in network load. The proposed algorithms alleviate the RTT fairness issue by controlling the upper limit of congestion window according to the delivery rate and queue status. In the Network Simulator 3 (NS3) simulation experiment, it shows that the adaptive congestion window of BBR (BBR-ACW) congestion control algorithm improves the fairness by more than 50% and reduces the queuing delay by 54%, compared with that of the original BBR in different buffer sizes.

Nearly 50% of the energy overhead in today's datacenters comes from host-to-host data transfers, which largely depend on the transport layer performance. Multipath TCP (MPTCP) has recently been suggested as a promising transport protocol to improve datacenter network throughput, yet it also increases the host CPU power consumption. It remains unclear whether datacenters can indeed benefit from using MPTCP from the perspective of energy efficiency. By analyzing the performance of MPTCP, we show that (1) despite consuming higher host CPU power than TCP, MPTCP can largely reduce the long flow completion time and thus save the aggregated energy; (2) link-sharing subflows in MPTCP not only has negative impact on both throughput-sensitive long flows and latency-sensitive short flows, but also noticeably increases the host CPU power, especially for short flows. We present MPTCP-D, an energy-efficient variant of multipath TCP for datacenters. MPTCP-D incorporates a novel congestion control algorithm that can provide energy efficiency by minimizing the flow completion time, and an extra subflow elimination mechanism that can preclude link-sharing subflows from increasing the host CPU power. We implement MPTCP-D in the Linux kernel and study its performance by experiments on Amazon EC2. Our results show that, without degrading the performance of the long flow throughput and short flow completion time, MPTCP-D reduces the long flow energy consumption by up to 72% compared to DCTCP for data transfers, and reduces the short flow power consumption by up to 46% compared to MPTCP with linksharing subflows.

Multipath Transmission Control Protocol (MPTCP) is in a rapid development process. MPTCP allows a network node to utilize multiple network interfaces and IP paths at the same time. It can take full advantage of network resources and provide reliable transmission, which brings advantages to users in terms of performance and reliability. In order to study the performance of MPTCP under high load, this paper uses Mininet to create an SDN environment and compare the performance differences between MPTCP and TCP under high load, and simulates a common Web application network architecture. The performance of MPTCP under this architecture is tested and analyzed. The test results show that MPTCP performs better than traditional TCP under high load. Besides, its performance can be optimized furtherly by adjusting related parameters.

Recently, requirement of ubiquitous availability and increase of mobile user driving traffic has become a serious problem. On the other hand, most mobile equipment have more than one network interface and a host can use multiple network interfaces simultaneously with multi-homing technology. Therefore, multiple paths are utilized for data transfer in one pair of terminals. This is a Con-current Multi-path Transfer (CMT). It makes acquire improved bandwidth and resilience just by modifying previous transport layer protocol like TCP (transmission control protocol). TCP is the most common transport layer protocol in Internet applications. Accordingly, IETF have proposed Multi-path TCP (MPTCP) that previous single-TCP with multi-homing technology. However, when transmit the packet using different two path which has dissimilar characteristic simultaneously, there is some throughput degradation compared to single-TCP that shows best performance. The reason is that out-of-ordered packet is generated because each path has different bandwidth, delay and packet loss rate. To solve these problems, this research proposes reordering considered packet scheduling algorithm at the sender. Main idea of the proposed scheme is estimating packet receiving time at the receiver and scheduling packet. By using proposed algorithm, the number of out-of-ordered packet in the receiver buffer is reduced. Therefore, performance degradation is also mitigated especially in the case that asymmetry between two path is severe.

The spread of Edge Internet of Things (IoT) radically changes our lifestyle. However, the multimedia services in edge IoT are still stuck by inefficiency. The dynamic typologies perplex the transmission of massive real-time data. To solve this problem, multipath transmission control protocol (MPTCP) which has a natural advantage in transmission robustness and bandwidth aggregation is becoming a good choice. In this paper, failure-aware and delay-predicted multipath virtual queue scheduling (FD-MVQS) is proposed to optimize the MPTCP performance in edge IoT. FD-MVQS constructs a two-plane cooperative scheduling system. In the control plane, the transmission failure estimation and chaos theory-based arrival delay prediction methods are introduced to provide the foundation for prescheduling. In the data plane, the multipath virtual queue scheduling is designed to allocate segments to different subflows. Simulation results showed that the proposed FD-MVQS performed better than standard and typical multipath transmission solutions in throughput, delay, and segment disorder.