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Introducing an Event-Based Architecture for Concurrent and Distributed Evolutionary Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11101))

Abstract

Cloud-native applications add a layer of abstraction to the underlying distributed computing system, defining a high-level, self-scaling and self-managed architecture of different microservices linked by a messaging bus. Creating new algorithms that tap these architectural patterns and at the same time employ distributed resources efficiently is a challenge we will be taking up in this paper. We introduce KafkEO, a cloud-native evolutionary algorithms framework that is prepared to work with different implementations of evolutionary algorithms and other population-based metaheuristics by using micro-populations and stateless services as the main building blocks; KafkEO is an attempt to map the traditional evolutionary algorithm to this new cloud-native format. As far as we know, this is the first architecture of this kind that has been published and tested, and is free software and vendor-independent, based on OpenWhisk and Kafka. This paper presents a proof of concept, examines its cost, and tests the impact on the algorithm of the design around cloud-native and asynchronous system by comparing it on the well known BBOB benchmarks with other pool-based architectures, with which it has a remarkable functional resemblance. KafkEO results are quite competitive with similar architectures.

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Acknowledgments

Supported by projects TIN2014-56494-C4-3-P (Spanish Ministry of Economy and Competitiveness) and DeepBio (TIN2017-85727-C4-2-P).

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Correspondence to Juan J. Merelo Guervós .

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Merelo Guervós, J.J., García-Valdez, J.M. (2018). Introducing an Event-Based Architecture for Concurrent and Distributed Evolutionary Algorithms. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_32

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  • DOI: https://doi.org/10.1007/978-3-319-99253-2_32

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  • Online ISBN: 978-3-319-99253-2

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