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The concept of a fully connected world in the fifth generation technology envisions the massive rollout of small and constrained Internet of Things (IoT) devices. In such IoT environments, machine-to-machine (M2M) communications is key for allowing these devices (e.g., sensors, actuators, smart meters/monitors) to collect data toward a remote server. We address this particular scenario by building and evaluating an IoT multi-access reference architecture, with a multipath TCP (MPTCP) gateway connected to multiple cellular networks to offload M2M data traffic. Experimental results from CoAP-emulated traffic indicate that the our proposed system together with the benefits of MPTCP improve IoT application performance. More specifically, multipath-based M2M data traffic offloading increases throughput and significantly reduces latency of CoAP requests as the number of sensor networks increases. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) of cellular networks aims at providing very high data rates, ultra-reliable low latency, and massive connection density. As one of the fundamental design trends toward these objectives, 5G exploits multi-connectivity (i.e., the concurrent use of multiple access networks), where multipath transport protocols have emerged as key technology enablers. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths and has a critical impact on the protocol performance. Within this context, we present in this article the first empirical evaluation of state-of-the-art multipath schedulers based on real 5G data, for both static and mobile scenarios. Furthermore, we introduce M-Peekaboo, which builds on a state-of-the-art learning-based multipath scheduler and extends its usage to 5G networks. Our results illustrate the benefits of employing a learning-based multipath scheduler for 5G networks and motivate further studies of advanced learning schemes that can adapt more quickly to the path conditions, and take into account the emerging features and requirements of 5G and beyond networks. [ABSTRACT FROM AUTHOR]

Most Internet hosts today support multiple access technologies and network interfaces. Multipath transport protocols, like MPTCP, are being deployed (e.g., in smartphones), allowing transparent simultaneous use of multiple links. Besides providing increased resilience to link failures, multipath transports may better exploit available (aggregate) capacity across all interfaces. The safest way to ensure fairness is to assume that any subflows of a multipath end-to-end connection may share bottleneck links, but knowledge of non-shared bottlenecks could allow multipath senders to exploit more capacity without being unfair to other flows. The problem of reliably detecting the existence of (non)-shared bottlenecks is not trivial and is compounded by the fact that bottlenecks may change due to traffic dynamics. In this paper we focus on practical methods to reliably group flows that share, possibly dynamic, bottlenecks online and in a passive manner (i.e., without injecting measurement traffic). We introduce a novel dynamic clustering algorithm that we apply to update our previous shared bottleneck flow grouping (SBFG) method standardized by the IETF, based on delay statistics. We also adapt an offline SBFG method based on wavelet filters to enable it for online operation. These SBFG methods are evaluated by a simple testbed, rigorous simulation and real-world Internet experiments in a testbed comprised of multihomed hosts. Our results suggest that there is no clear winner, and selection of the “best” SBFG method will have to consider tradeoffs regarding accuracy, lag, and application requirements. [ABSTRACT FROM AUTHOR]

Multipath transport protocols utilize multiple network paths (e.g., WiFi and cellular) to achieve improved performance and reliability, compared with their single-path counterparts. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths. However, state-of-the-art multipath schedulers face the challenge when dealing with heterogeneous paths with dynamic path characteristics (i.e., packet loss, fluctuation of delay). In this paper, we propose Peekaboo, a novel learning-based multipath scheduler that is aware of the dynamic characteristics of the heterogeneous paths. Peekaboo is able to learn scheduling decisions to adopt over time based on the current path characteristics and dynamicity levels - from both deterministic and stochastic perspectives. We implement Peekaboo in Multipath QUIC (MPQUIC) and compare it with state-of-the-art multipath schedulers for a wide range of dynamic heterogeneous environments, upon both emulated and real networks. Our results show that Peekaboo outperforms the other schedulers by up to 31.2% in emulated networks and up to 36.3% in real network scenarios. [ABSTRACT FROM AUTHOR]

The demand for mobile communication is continuously increasing, and mobile devices are now the communication device of choice for many people. To guarantee connectivity and performance, mobile devices are typically equipped with multiple interfaces. To this end, exploiting multiple available interfaces is also a crucial aspect of the upcoming 5G standard for reducing costs, easing network management, and providing a good user experience. Multi-path protocols, such as multi-path TCP (MPTCP), can be used to provide performance optimization through load-balancing and resilience to coverage drops and link failures, however, they do not automatically guarantee better performance. For instance, low-latency communication has been proven hard to achieve when a device has network interfaces with asymmetric capacity and delay (e.g., LTE and WLAN). For multi-path communication, the data scheduler is vital to provide low latency, since it decides over which network interface to send individual data segments. In this paper, we focus on the MPTCP scheduler with the goal of providing a good user experience for latency-sensitive applications when interface quality is asymmetric. After an initial assessment of existing scheduling algorithms, we present two novel scheduling techniques: the block estimation (BLEST) scheduler and the shortest transmission time first (STTF) scheduler. BLEST and STTF are compared with existing schedulers in both emulated and real-world environments and are shown to reduce web object transmission times with up to 51% and provide 45% faster communication for interactive applications, compared with MPTCP’s default scheduler. [ABSTRACT FROM AUTHOR]

Over the past years, TCP has gone through numerous updates to provide performance enhancement under diverse network conditions. However, with respect to losses, little can be achieved with legacy TCP detection and recovery mechanisms. Both fast retransmission and retransmission timeout take at least one extra round trip time to perform, and this might significantly impact the performance of latency-sensitive applications, especially in lossy or high delay networks. While forward error correction (FEC) is not a new initiative in this direction, the majority of the approaches consider FEC inside the application. In this paper, we design and implement a framework, where FEC is integrated within TCP. Our main goal with this design choice is to enable latency sensitive applications over TCP in high delay and lossy networks, but remaining application agnostic. We further incorporate this design into multipath TCP (MPTCP), where we focus particularly on heterogeneous settings, considering the fact that TCP recovery mechanisms further escalate head-of-line blocking in multipath. We evaluate the performance of the proposed framework and show that such a framework can bring significant benefits compared with legacy TCP and MPTCP for latency-sensitive real application traffic, such as video streaming and web services. [ABSTRACT FROM AUTHOR]