{"ID":5551954,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T04:46:53.079013169Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00455","arxiv_id":"2607.00455","title":"CloudyGUI: A Novel Python-based Framework for Auto-Scaling and Cloud Workload Analysis","abstract":"Purpose: Cloud computing environments are highly dynamic, creating major challenges for resource management. Accurate workload prediction is therefore essential for effective auto-scaling. To address this, we present CloudyGUI, a Python simulation framework with an easy-to-use GUI that allows researchers to test and validate resource management strategies. Methods: This framework employs a three-stage pipeline: workload generation, prediction (utilizing XGBoost and LSTM), and an auto-scaling system based on the MAPE loop. Validation includes internal, intermediate, and external methods to ensure system reliability. Results: CloudyGUI's generated workloads closely match real-world datasets. A two-sample K-S test confirms this alignment, showing strong p-values of 0.19 for CPU and 0.14 for memory. When compared to a command-line tool, the GUI adds only a minimal overhead of 1.4x-4.67x. Furthermore, expert review validates the tool's realism and practical usefulness. Conclusion: CloudyGUI fills a critical gap by providing an accessible and efficient platform for simulating auto-scaling in cloud applications, helping researchers develop advanced cloud management solutions.","short_abstract":"Purpose: Cloud computing environments are highly dynamic, creating major challenges for resource management. Accurate workload prediction is therefore essential for effective auto-scaling. To address this, we present CloudyGUI, a Python simulation framework with an easy-to-use GUI that allows researchers to test and va...","url_abs":"https://arxiv.org/abs/2607.00455","url_pdf":"https://arxiv.org/pdf/2607.00455v1","authors":"[\"Jyoti Bawa\",\"Mohit Kaushik\",\"Kuljit Kaur Chahal\",\"Kamaljit Kaur\"]","published":"2026-07-01T05:20:09Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[]","has_code":false}