🤖 AI Summary
This study addresses the strong dynamism of cloud environments, which demands accurate workload prediction to enable efficient autoscaling. The authors propose a Python-based graphical simulation framework that integrates workload generation, XGBoost and LSTM prediction models, and an MAPE-driven autoscaling mechanism into an end-to-end three-stage simulation pipeline. Notably, this framework uniquely combines an intuitive GUI with multi-level validation—internal, intermediate, and external—to balance realism and accessibility. Experimental results demonstrate that the synthetically generated workloads closely resemble real-world data, with Kolmogorov–Smirnov test p-values of 0.19 for CPU and 0.14 for memory usage. The GUI introduces only modest overhead (1.4×–4.67×) and has received expert endorsement, effectively filling a critical gap in cloud autoscaling research tooling.
📝 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.