Reinforcement Learning-Assisted Genetic Programming Hyper Heuristic Approach to Location-Aware Dynamic Online Application Deployment in Clouds

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{yan:2024:GECCO2,

• author = "Longfei Felix Yan and Hui Ma and Gang Chen2",
• title = "Reinforcement {Learning-Assisted} Genetic Programming Hyper Heuristic Approach to {Location-Aware} Dynamic Online Application Deployment in Clouds",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference",
• year = "2024",
• editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
• pages = "988--997",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, genetic programming hyper heuristic, reinforcement learning, surrogate model, application deployment",
• isbn13 = "979-8-4007-0494-9",
• DOI = "doi:10.1145/3638529.3654058",
• size = "10 pages",
• abstract = "Location-Aware Dynamic Online Application dePloyment (LADOAP) in clouds is an NP-hard combinatorial optimisation problem. Genetic Programming Hyper-Heuristic (GPHH) has emerged as a promising approach for addressing LADOAP demands by dynamically generating Virtual Machine (VM) selection heuristics online. However, the performance of GPHH is impeded by long simulation times and low sampling efficiency. In this paper, we propose a novel hyper-heuristic framework that integrates Genetic Programming Hyper-Heuristic (GPHH) and Reinforcement Learning (RL) approaches to evolve rules for efficiently selecting location-aware Virtual Machines (VMs) capable of hosting multiple containers. The RL policy's value function acts as a surrogate model, significantly expediting the evaluation of generated VM selection rules. By applying this hybrid framework to LADOAP problems, we achieve competitive performance with a notable reduction in the number of required simulations. This innovative approach not only enhances the efficiency of VM selection but also contributes to advancing the state-of-the-art in addressing complex LADOAP challenges.",
• notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Longfei Felix Yan Hui Ma Aaron Chen

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