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Speed up genetic algorithms in the cloud using software containers
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DOI：10.1016/j.future.2018.09.066.png)
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Scalability issues might prevent Genetic Algorithms from being applied to real world problems. Exploiting parallelisation in the cloud might be an affordable approach to getting time efficient solutions that benefit of the appealing features of scalability, resource discovery, reliability, fault-tolerance and costeffectiveness. Nevertheless, parallel computation is very prone to cause considerable overhead for communication. Also, making Genetic Algorithms distributed in an on-demand fashion is not trivial. Aiming at keeping under control the communication cost and, at the same time, supporting developers in the construction and deployment of parallel Genetic Algorithms, we designed and implemented a novel approach to distribute Genetic Algorithms in the form of a cloud-based application. It is based on the master/slave model, exploiting software containers, their cloud orchestration and message queues. We also devised a conceptual workflow covering each cloud Genetic Algorithms distribution phase, from resources allocation to actual deployment and execution, in an engineered fashion. Then, the application performance has been evaluated using a benchmark problem. According to the performance and setup times results, it emerged that the cloud can be considered a compelling way of scaling Genetic Algorithms and an excellent alternative to other technologies strictly related to the physically owned hardware. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Genetic algorithms
Parallel genetic algorithms
Cloud computing
Software containers
Software engineering
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