Abstract
With the increasing number of cloud services in multi-cloud, it has been a challenging task to choose suitable cloud services in consideration of multiple conflicting objectives. Multi-objective location-aware service brokering in multi-cloud aims to find a set of trade-off solutions that minimize both the cost and latency. To achieve this goal, existing approaches either manually design brokering heuristics or automatically generate heuristics via Genetic Programming Hyper-Heuristics (GPHH) for each problem domain from scratch. However, manually designing heuristics takes a long time and requires domain knowledge. Also, knowledge learnt from one problem domain can be helpful for solving another problem domain. To effectively and efficiently generate heuristics for any new problem domain, we propose three novel GPHH-based approaches with transfer learning to automatically generate a group of Pareto-optimal heuristics. Experimental evaluations on real-world datasets demonstrate that our proposed GPHH with transfer learning approaches can outperform existing approaches.
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Chen, Y., Shi, T., Ma, H., Chen, G. (2023). Multi-objective Location-Aware Service Brokering in Multi-cloud - A GPHH Approach with Transfer Learning. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_37
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