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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References
Atienza, J., Castillo, P.A., García, M., González, J., Merelo, J.: Jenetic: a distributed, fine-grained, asynchronous evolutionary algorithm using Jini. In: Wang, P.P. (ed.) Proceedings of JCIS 2000 (Joint Conference on Information Sciences), vol. I, pp. 1087–1089 (2000). ISBN: 0-9643456-9-2
Baldini, I., et al.: Cloud-native, event-based programming for mobile applications. In: Proceedings - International Conference on Mobile Software Engineering and Systems, MOBILESoft 2016, pp. 287–288 (2016)
Baugh, J.W., Kumar, S.V.: Asynchronous genetic algorithms for heterogeneous networks using coarse-grained dataflow. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 730–741. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45105-6_88
Bollini, A., Piastra, M.: Distributed and persistent evolutionary algorithms: a design pattern. In: Poli, R., Nordin, P., Langdon, W.B., Fogarty, T.C. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 173–183. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48885-5_14
Chong, F.S., Langdon, W.B.: Java based distributed genetic programming on the internet. In: Banzhaf, W., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, p. 1229. Morgan Kaufmann, Orlando, 13–17 July 1999. Full text in technical report CSRP-99-7
Coleman, V.: The DEME mode: an asynchronous genetic algorithm. Technical report, University of Massachussets at Amherst, Department of Computer Science (1989). uM-CS-1989-035
Fortin, F.A., Rainville, F.M.D., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)
García-Sánchez, P., González, J., Castillo, P.A., Arenas, M.G., Merelo-Guervós, J.: Service oriented evolutionary algorithms. Soft Comput. 17(6), 1059–1075 (2013)
García-Valdez, M., Trujillo, L., Merelo, J.J., Fernández de Vega, F., Olague, G.: The EvoSpace model for pool-based evolutionary algorithms. J. Grid Comput. 13(3), 329–349 (2015). https://doi.org/10.1007/s10723-014-9319-2
Hansen, N., Auger, A., Mersmann, O., Tusar, T., Brockhoff, D.: COCO: a platform for comparing continuous optimizers in a black-box setting (2016). arXiv preprint arXiv:1603.08785
Hansen, N., Auger, A., Ros, R., Finck, S., Pošík, P.: Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In: Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1689–1696. ACM (2010)
Merelo-Guervós, J.J., Arenas, M.G., Mora, A.M., Castillo, P.A., Romero, G., Laredo, J.L.J.: Cloud-based evolutionary algorithms: an algorithmic study. CoRR abs/1105.6205, 1–7 (2011)
Munawar, A., Wahib, M., Munetomo, M., Akama, K.: The design, usage, and performance of GridUFO: a grid based unified framework for optimization. Future Gener. Comput. Syst. 26(4), 633–644 (2010)
Papazoglou, M.P., van den Heuvel, W.J.: Service oriented architectures: approaches, technologies and research issues. VLDB J. 16(3), 389–415 (2007). https://doi.org/10.1007/s00778-007-0044-3
Rodríguez, L.G., Diosa, H.A., Rojas-Galeano, S.: Towards a component-based software architecture for genetic algorithms. In: 2014 9th Computing Colombian Conference (9CCC), pp. 1–6, September 2014
Salza, P.: Parallel genetic algorithms in the cloud. Ph.D. thesis, University of Salerno, Italy (2017). https://goo.gl/sDx6mY
Salza, P., Hemberg, E., Ferrucci, F., O’Reilly, U.M.: cCube: a cloud microservices architecture for evolutionary machine learning classification. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 137–138. ACM (2017)
Tanabe, R., Fukunaga, A.: Evaluation of a randomized parameter setting strategy for island-model evolutionary algorithms. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 1263–1270. IEEE (2013)
Thönes, J.: Microservices. IEEE Softw. 32(1), 116–116 (2015)
Varghese, B., Buyya, R.: Next generation cloud computing: new trends and research directions. Future Gener. Comput. Syst. 79, 849–861 (2018). Cited by 2
Voigt, H.-M., Born, J., Santibañez-Koref, I.: Modelling and simulation of distributed evolutionary search processes for function optimization. In: Schwefel, H.-P., Männer, R. (eds.) PPSN 1990. LNCS, vol. 496, pp. 373–380. Springer, Heidelberg (1991). https://doi.org/10.1007/BFb0029778
Zorman, B., Kapfhammer, G.M., Roos, R.S.: Creation and analysis of a JavaSpace-based distributed genetic algorithm. In: PDPTA, pp. 1107–1112 (2002)
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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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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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