Learning to generate Reliable Broadcast Algorithms

Modern distributed systems are supported by fault-tolerant algorithms, like Reliable Broadcast and Consensus, that assure the correct operation of the system even when some of the nodes of the system fail. However, the development of distributed algorithms is a manual and complex process, resulting...

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Published in:arXiv.org 2022-07
Main Authors: Vaz, Diogo, Matos, David R, Pardal, Miguel L, Correia, Miguel
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Language:English
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Algorithms
Computer networks
Fault tolerance
Intelligent agents
Machine learning
Scientific papers
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Correia, Miguel
description Modern distributed systems are supported by fault-tolerant algorithms, like Reliable Broadcast and Consensus, that assure the correct operation of the system even when some of the nodes of the system fail. However, the development of distributed algorithms is a manual and complex process, resulting in scientific papers that usually present a single algorithm or variations of existing ones. To automate the process of developing such algorithms, this work presents an intelligent agent that uses Reinforcement Learning to generate correct and efficient fault-tolerant distributed algorithms. We show that our approach is able to generate correct fault-tolerant Reliable Broadcast algorithms with the same performance of others available in the literature, in only 12,000 learning episodes.
doi_str_mv 10.48550/arxiv.2208.00525
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subjects Algorithms
Computer networks
Fault tolerance
Intelligent agents
Machine learning
Scientific papers
title Learning to generate Reliable Broadcast Algorithms
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