Prabhavat, Sumet

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Prabhavat, Sumet
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Prabhavat, S.
Prapawat, Sumet
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sumet.pr@kmitl.ac.th

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    Item type:Publication,
    A new explicit congestion notification scheme for satellite IP networks
    (2016-11-01)
    Utsumi, Satoshi
    ;
    Zabir, Salahuddin Muhammad Salim
    ;
    Prabhavat, Sumet
    Longer transmission delay and higher link errors affect data communication performance over satellite links. The problem gets compounded in case a congestion occurs in the corresponding data path. Taking preventive measures before a congestion actually occurs can help in avoiding such situations. Irrespective of the network characteristics, Explicit Congestion Notification (ECN) either through ICMP messages or through marking packets aims to achieve this objective. However, use of ICMP messages for explicit congestion notification leads to vulnerability to attacks by malicious hosts. On the other hand, use of conventional marking schemes that mark data packets in the forward direction can be slow as they have to traverse the satellite links. In this manuscript, we propose a new mechanism of ECN for an even faster notification of an incipient congestion over satellite IP networks. Our proposal, Mark-Reverse ECN, marks packets corresponding to acknowledgement (ACK) segments in the reverse direction to the sender. This leads to a reduction of the latency between detection and actual notification. As such, our mechanism leads to a more accurate and effective approach to congestion control. In addition, our proposed mechanism is free from vulnerability to malicious attacks. We also develop a new analytical model for Mark-Reverse ECN on TCP Reno for performance prediction and validation of simulation outcomes. Simulation results show that our proposed mechanism (i) leads to up to 25% improvement of TCP performance, (ii) reduces packet loss at the congested router by down to about 0% and (iii) helps to improve the performance of conventional TCP when deployed together.
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    Item type:Publication,
    Deep Learning-Based Early Detection and Avoidance of Traffic Congestion in Software-Defined Networks
    (2022-01-01)
    Prabhavat, Sumet
    ;
    Thongthavorn, Thananop
    ;
    Pasupa, Kitsuchart
    Software-defined Networking (SDN) provides an easy way to monitor network and traffic conditions by employing software-based controllers to communicate with the hardware directly. It provides helpful information that enables efficient routing decisions. This research study attempted to use deep learning techniques - Long Short-term Memory, Bidirectional Long Short-term Memory, and Gated Recurrent Unit - to predict network traffic to allow the controller to early detect congestion. The traffic flow in a network link that will likely be congested will be rerouted to a new path with the largest available bandwidth. Various scenarios were simulated to evaluate our deep learning-based SDN controller (Ryu controller platform). The results show that our proposed deep learning-based SDN controller outperformed the traditional load balancing technique.