TCP-Puerto-Londero: A New Approach for End-to-End Queue Length Control
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Abstract
This paper presents a new TCP protocol called TCPPuerto-Londero, which makes dynamic tuning in the congestion window (cwnd) by means of adaptive control theory aiming to keep stable and small the queue length in the bottleneck link located on the path from source to destination. This adaptive control loop has the relative delay in the forward path measured as an input and the cwnd value as an output. Thus, unlike classic TCP protocols, TCP-Puerto-Londero does not use Round Trip Time (RTT) information. Also, unlike classic Active Queue Management (AQM) strategies and Explicit Congestion Notification (ECN) based protocols, TCP-Puerto-Londero employs end-to-end queue management without the need for ECN resources. Moreover, TCP-Puerto-Londero also aims to attend to the new challenges of the Industrial Internet of Things (IIoT). Its performance has been tested in a Dumbbell network topology shared by TCP and UDP-like Networked Control Systems (NCS) flows. Therefore, TCP-Puerto-Londero performance has been compared with ECN-based protocols like DCTCP, E-DCTCP, TCP-Jersey, and ENCN. Furthermore, this paper employs an approach for modeling, analysis, simulation, and verification of the communication network and NCS employing UPPAAL simulation software tool, where all network constituents (transmitters, channels, routers, receivers, controllers, and plants) were modeled employing timed automata, simplifying a formal verification of the complete studied system. Simulations and statistical verification indicate that even though utilizing fewer resources (as it does not require the AQM/ECN information) TCP-Puerto-Londero overcomes TCP-Jersey, DCTCP, and EDCTCP with regard to throughput and fairness for TCP flows. Also, TCP-Puerto-Londero flows are capable to keep the queue length more stable and smaller than other protocols that were compared, and consequently, it reduces the impact in NCS-UDPlike flows sharing the same network, whose performance was measured employing the Integral Time Absolute Error (ITAE), which is a desirable feature for Industrial IoT.
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