[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82517-en":3,"doc-seo-82517-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82517,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1782698725881665579",8,"Research & Report","CloudyGUI A Novel Python based Framework for Auto-Scaling and Cloud Workload Analysis","Cloud computing environments are highly dynamic, creating major challenges for resource management and making accurate workload prediction essential for effective auto-scaling. CloudyGUI provides a Python simulation framework with an easy-to-use GUI so researchers can test and validate resource management strategies. The workflow uses a three-stage pipeline for workload generation, prediction via XGBoost and LSTM, and auto-scaling driven by a MAPE loop. Workloads closely match real datasets, verified through internal, intermediate, and external validation plus a two-sample K-S test; GUI overhead is minimal versus command-line tools.","CloudyGUI: A Novel Python-based Framework for Auto-Scaling and Cloud Workload  \nAnalysis  \nJyoti Bawa*  \nGuru Nanak Dev University, Department of Computer Science, Amritsar, 143005, Punjab, India  \nMohit Kaushik  \nGuru Nanak Dev University, Department of Computer Science, Amritsar, 143005, Punjab, India  \nKuljit Kaur Chahal  \nGuru Nanak Dev University, Department of Computer Science, Amritsar, 143005, Punjab, India  \nKamaljit Kaur  \nGuru Nanak Dev University, Department of Engineering and Technology, Amritsar, 143005, Punjab, India  \nAbstract  \nPurpose: 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 1.4×-4.67× . 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.  \nKeywords: cloud computing, simulator, workload analysis, auto-scaling, resource management  \n1. Introduction  \nCloud environments are highly complex due to virtualization, multi-tenancy, and auto-scaling [1, 2, 3, 4] . This makes it difficult for researchers to test their policies in a real cloud environment [1] . Researchers often use simulation tools to analyze cloud performance and resource behavior without needing actual cloud systems [2, 5] . However, traditional simulators often fail to accurately replicate the dynamic behavior of the cloud environment [1, 6, 7, 8, 9, 10] . To overcome this, we need a specialized simulation tool that can accurately cap-  \nJava-based simulators often face difficulties related to adaptability and flexibility, which are necessary to accurately simulate the complexities of modern cloud environments [17, 9] . Nowadays, a major challenge involves implementing and evaluating various auto-scaling techniques [17, 18, 19] .  \nTo address these limitations, more flexible and modern simulation tools are needed [13, 15, 16] . Tools like Cloudy [9] recently started a trend toward Python-based solutions. Python is preferred for its simplicity, readability, and easy integration with various libraries for GPU computing. However, This transition has been slow, and several challenges remain.  \narXiv :2607 .00455v 1 [ cs .DC] 1 Jul 2026  \nture these complexities. Such tools are essential for evaluating strategies for resource provisioning, load balancing, and energy efficiency [11, 12] . Thus, researchers can run repeated experiments without the expense of real-world cloud deployment [13, 1, 14] .  \nExisting tools are mostly built in Java and C++, with CloudSim serving as a key resource [15, 16] . However, these  \nPublished in Simulation Modelling Practice and Theory. Cite the published version: [https://doi.org/10.1016/j.simpat.2026.103308](https://doi.org/10.1016/j.simpat.2026.103308)  \nWhile Python offers features like simplicity and integration with AI/ML libraries, existing efforts often resulted in minimal innovation, such as projects that primarily translated older Java frameworks (e.g., geoCloudSim) or became inactive for extended periods (e.g., pyCloudSim) . These issues underscore the exist","cbCaih56C0bJuqJ2","https://ap.wps.com/l/cbCaih56C0bJuqJ2","pdf",2675077,3,1,27,"English","en",105,"# Introduction\n## Problem and Motivation\n## Related Work and Limitations\n## Contributions and Paper Organization\n# CloudyGUI Framework (Overview)\n# Methodology\n## Workload Generation\n## Workload Prediction\n## Auto-Scaling Loop\n# Validation and Results\n## Dataset Alignment\n## Statistical Testing\n## Performance Overhead\n# Conclusion","[{\"question\":\"What problem does CloudyGUI address in cloud auto-scaling research?\",\"answer\":\"CloudyGUI targets the difficulty of evaluating auto-scaling and resource management policies in highly dynamic cloud environments, where traditional simulators may not replicate cloud behavior accurately and researchers often lack an accessible testing platform.\"},{\"question\":\"How does CloudyGUI perform workload prediction and auto-scaling?\",\"answer\":\"CloudyGUI uses a three-stage pipeline: workload generation, prediction using XGBoost and LSTM, and an auto-scaling system based on a MAPE loop.\"},{\"question\":\"How is CloudyGUI validated and what do the results indicate?\",\"answer\":\"Validation uses internal, intermediate, and external methods, and generated workloads closely match real-world datasets. A two-sample K-S test reports strong p-values for CPU and memory, and GUI overhead versus a command-line tool is minimal.\"}]",1784181095,68,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cloudygui-a-novel-python-based-framework-for-auto-scaling-and-cloud-workload-analysis","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/cloudygui-a-novel-python-based-framework-for-auto-scaling-and-cloud-workload-analysis/82517/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-23","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does CloudyGUI address in cloud auto-scaling research?","Question",{"text":75,"@type":76},"CloudyGUI targets the difficulty of evaluating auto-scaling and resource management policies in highly dynamic cloud environments, where traditional simulators may not replicate cloud behavior accurately and researchers often lack an accessible testing platform.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does CloudyGUI perform workload prediction and auto-scaling?",{"text":80,"@type":76},"CloudyGUI uses a three-stage pipeline: workload generation, prediction using XGBoost and LSTM, and an auto-scaling system based on a MAPE loop.",{"name":82,"@type":73,"acceptedAnswer":83},"How is CloudyGUI validated and what do the results indicate?",{"text":84,"@type":76},"Validation uses internal, intermediate, and external methods, and generated workloads closely match real-world datasets. A two-sample K-S test reports strong p-values for CPU and memory, and GUI overhead versus a command-line tool is minimal.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]