{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automated-cloud-provisioning-on-aws-using","title":"Automated Cloud Provisioning on AWS using Deep Reinforcement Learning","arxiv_id":"1709.04305","date":"2017-09-13","proceeding":null,"authors":["Zhiguang Wang","Chul Gwon","Tim Oates","Adam Iezzi"],"abstract":"As the use of cloud computing continues to rise, controlling cost becomes\nincreasingly important. Yet there is evidence that 30\\% - 45\\% of cloud spend\nis wasted. Existing tools for cloud provisioning typically rely on highly\ntrained human experts to specify what to monitor, thresholds for triggering\naction, and actions. In this paper we explore the use of reinforcement learning\n(RL) to acquire policies to balance performance and spend, allowing humans to\nspecify what they want as opposed to how to do it, minimizing the need for\ncloud expertise. Empirical results with tabular, deep, and dueling double deep\nQ-learning with the CloudSim simulator show the utility of RL and the relative\nmerits of the approaches. We also demonstrate effective policy transfer\nlearning from an extremely simple simulator to CloudSim, with the next step\nbeing transfer from CloudSim to an Amazon Web Services physical environment.","url_abs":"http://arxiv.org/abs/1709.04305v2","url_pdf":"http://arxiv.org/pdf/1709.04305v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automated-cloud-provisioning-on-aws-using","repo_url":"https://github.com/csgwon/AWS-RL-Env","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"cloud-computing","task_name":"Cloud Computing"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}