Harnessing ML For Network Protocol Assessment: A Congestion Control Use Case
Pages 213 - 219
Abstract
In this paper, our primary objective is to showcase that the application of machine learning techniques extends beyond network protocol design. We aim to demonstrate that performance assessment of network protocols, a vital aspect of improving network infrastructures and developing better protocol designs, can be modernized through the utilization of machine learning. As a step towards this goal, we have designed and introduced Mahak, the first tool that harnesses active learning techniques to automate the performance assessment of congestion control schemes. Mahak actively learns to optimize the evaluation process of congestion control schemes so that they can generate their performance maps over a desired space without exhaustively testing them in every scenario. Mahak treats schemes under the test as black boxes. This protocol-agnostic aspect of Mahak enables users to directly assess the performance of the actual implementation of a protocol instead of their over-simplified mathematical models or simplified simulated versions.
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Index Terms
- Harnessing ML For Network Protocol Assessment: A Congestion Control Use Case
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Published In
November 2023
306 pages
ISBN:9798400704154
DOI:10.1145/3626111
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Published: 28 November 2023
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HotNets '23
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HotNets '23: The 22nd ACM Workshop on Hot Topics in Networks
November 28 - 29, 2023
MA, Cambridge, USA
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