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Fine-Grained Flow Control Agent on Path MTU for IoT Software

Published: 05 October 2023 Publication History

Abstract

Internet of Things (IoT) software is used to control the distributed hardware of the underlying network and provide a reliable operating platform for various services. In production system, diversity IoT software provides multiple services, flows of different software run in parallel on the same IoT platform. Thus, system-level network parameter configurations may not be suitable for all service needs. In this paper, we focus on the challenge of the personally parameterizing transmission unit size and congestion windows (CWND) in flow control. We propose deeper flow control software model (DeepFC) for finer-grained flow control than traditional algorithms. DeepFC consist of two parts: (i) Since system-level transmission unit size may degrade network performance due to frequent fragmentation, we combine path MTU (PMTU) and deep reinforcement learning (DRL) to predict fine-grained flow-level transmission unit size. (ii) Transmission unit size is related to CWND in flow control. The fine-grained transmission unit size needs fine-grained congestion control solution. In DeepFC, we consider the mutual coupling between transmission unit size and CWND parameter configuration to further improve network performance. Experimental results show that DeepFC can reduce fragmentation by 67.8% compared to the protocols with system-level transmission unit size, flow completion time can be reduced by 20.93%, and throughput can be increased by 19.96% compared to the average of benchmark algorithms.

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    Internetware '23: Proceedings of the 14th Asia-Pacific Symposium on Internetware
    August 2023
    332 pages
    ISBN:9798400708947
    DOI:10.1145/3609437
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    Published: 05 October 2023

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    Author Tags

    1. Deep Reinforcement Learning
    2. Flow Control
    3. Internet of Things
    4. Path Maximum Transmission Unit

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