Solving MP-TCP‘s Shared Bottlenecks Using a SDN with OpenDayLight as the Controller

Network functions virtualization (NFV) offers an alternative way to design, deploy, and manage networking services. This paper presents the implementation and deployment of a virtualized network function to solve MP-TCP’ shared bottlenecks problem. MP-TCP (MultiPath-TCP) is a protocol that allows hosts that have several interfaces to send data through multiple paths in the network. An MP-TCP connection is divided into several TCP connections called subflows that may follow different paths, in this way effective connection rates and resilience can be increased. However, in a scenario where MP-TCP is used with hosts having a single network interface, created subflows follow the same path even if several are available; this generates the problem known as a shared bottleneck. A solution to this problem is implemented as a SDN (Software Defined Networking) module that is integrated into OpenDayLight’s framework which is used as the SDN controller. This solution is then deployed as a NFV over a cloud infrastructure implemented with OpenStack over a high-end physical server. The deployment of the NFV is automated to a certain degree using Ansible, a DevOps tool. Finally, results of the tests of the SDN solution to MP-TCP’s shared bottlenecks and its operation as a NFV using a completely virtualized network are reported.

Multipath Transmission Control Protocol (MPTCP) is an approach towards high-throughput and efficient load balancing over multiple paths. Each of paths forms a TCP connection with an IP address, and those can be implemented as multiple network interfaces or multiple ports within a network interface. In this paper, we focus on the multiple network interfaces environment. Each network interface with an IP address is called as a subflow. A subflow is a TCP connection which can have a different internet path identified by IP addresses of source and destination network interfaces. To control these multiple subflows, MPTCP supports many options. Specifically, to establish a new subflow, MPTCP uses an <monospace>ADD&#x005F;ADDR</monospace> option. A host sends <monospace>ADD&#x005F;ADDR</monospace> option to inform another host of its IP address, and then, the host receiving <monospace>ADD&#x005F;ADDR</monospace> option tries to establish a subflow at the address of <monospace>ADD&#x005F;ADDR</monospace> option. However, by forging the <monospace>ADD&#x005F;ADDR</monospace> option, an attacker can create a fake subflow that passes through itself and eventually hijack the connection between both end hosts. In a previous study, Hash-based Message Authentication (HMAC) was added to the <monospace>ADD&#x005F;ADDR</monospace> option, preventing it from being forged. Nevertheless, since the keys for generating HMAC can be leaked during three-way handshake, a variant of the <monospace>ADD&#x005F;ADDR</monospace> attack called the persistent <monospace>ADD&#x005F;ADDR</monospace> attack can be possible. To this end, we propose a protocol that can prevent the <monospace>ADD&#x005F;ADDR</monospace> attacks by backward confirmation of the <monospace>ADD&#x005F;ADDR</monospace> option without encryption. The main idea of our proposal is to apply a digital signature scheme for the backward confirmation. We show security analysis for the proposed protocol and compare with the previous studies in terms of time/space overheads.

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 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.

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.

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 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.

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.

The number of mobile terminals that have both a cellular interface and a Wi-Fi interface has been growing in the recent years. Meanwhile, Multipath TCP (MPTCP) that enables the simultaneous use of multiple paths with multiple TCP flows while showing just one regular TCP connection with the application has been standardized by the IETF and has drawn more attention recently. One feature of MPTCP is that it can be realized only by operating system layers supporting multipath communication without any modifications of the application. However, it might be difficult to install MPTCP in all server operating systems. In this paper, we propose a method for applying a proxy that forwards TCP communication from a server to MPTCP communication on a mobile core network, and show the results of evaluation with the existing equipment.

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.

&lt;div style="text-align: start;" align="center"&gt;&lt;span lang="EN-US"&gt;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).&lt;/span&gt;&lt;/div&gt;

A path characteristics based adaptive subflow switching algorithm for dynamic heterogeneous MPTCP network

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.

This paper focuses on evaluating the use of MPTCP to forward sub flows in Open Flow networks. MPTCP is a network protocol designed to forward sub flows through disjointed paths. Modern networks commonly use Equal-Cost Multipath Protocol (ECMP) to split flows through distinct paths. However, even with ECMP enabled, sub flows may be forwarded through the same path. MPTCP improves the multipath routing by setting sub flows to be forwarded through distinct paths. As a consequence, the amount of sub flows must be considered to evaluate the network throughput. In this paper, we design Multiflow to use MPTCP in Open Flow networks. Our proposal is to improve the throughput in shared bottlenecks by forwarding sub flows from a same MPTCP connection through multiple paths. We validate our approach in a test bed where shared bottlenecks occur in the link at the endpoints. The Multiflow improvement of the network performance is evaluated in experiments about resilience and end-to-end throughput.