[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126332-en":3,"doc-seo-126332-105":31,"detail-sidebar-cat-0-en-105":93},{"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":28,"seo_description":14,"update_tm":29,"read_time":30},126332,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",6,"Technology","Automating cloud virtual machines allocation via machine learning","Healthcare applications increasingly rely on cloud computing, yet existing approaches remain rigid when network and virtual machine resources change during execution. Medical data stored and processed in the cloud as virtual resources requires optimization of virtual node and data placement to improve processing time. Cloud network security is further constrained by dynamic topology, limiting traditional firewalls. The work proposes a divided-cloud design with zone controllers, and applies machine learning methods (decision tree and LDA for controller placement; K-neighbours for VM location) to reduce congestion and achieve high placement accuracy.","Automating cloud virtual machines allocation via machine  \nlearning  \nFerdaous Kamoun-Abid, Hounaida Frikha, Amel Meddeb-Makhoulf, Faouzi Zarai  \nNTS’COM Research Unit Sfax, ENET’COM Sfax, Sfax, Tunisia  \nArticle history:  \nReceived Jan 3, 2024 Revised Mar 2, 2024 Accepted Mar 30, 2024  \nKeywords:  \nController Divided-cloud Firewall  \nKNN  \nLDA-decision tree Machine learning Virtual machine  \nCorresponding Author:  \nIn the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering traditional firewalls ’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83% .  \nThis is an open access article under the CC BY-SA license.  \nFerdaous Kamoun-Abid  \nNTS’COM Research Unit Sfax, ENET’COM Sfax Sfax, Tunisia  \nEmail: [abidkamounferdaous@gmail.com](abidkamounferdaous@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe proliferation of information utilization has led to the adoption of cloud computing, an expeditious technology that offers adaptable, cost-efficient, and easily manageable access to potent computing and storage resources on demand. The integration of cloud computing into various sectors, such as medical applications, is particularly intriguing due to its scalability and elasticity [1] . Additionally, it is a cutting-edge technology heavily influenced by modern medical monitoring systems. A major challenge faced today is the secure delivery of cloud-based services to medical clients. The challenge arises from the inability of cloud service providers (CSPs) to assure data security when shared among multiple cloud customers [2] . cloud computing represents a rapidly evolving technology that offers cost-effective, flexible, and on-demand resource access. Its cornerstone is virtualization, which minimizes initial investments. Essentially, physical machines (PMs) enable the creation of numerous virtual machines (VMs) for managing medical services and information. Nonetheless, handling cloud storage introduces fresh hurdles concerning data security.  \nThe studies in [3], [4] examined the effects of distributed firewalls. Although previous studies have explored the impact of the added a new improvements and functionalities to traditional firewalls. But they have not explicitly addressed the influence of the rules that are set on the architecture of mobile topologies such as cloud computing.  \nTraditional firewalls may lose their effectiveness due to the intricate nature of various network topologies. To address this issue, a cost-effective solution known as a distributed firewall has been developed [3]. This firewall is created using open-source tools, which bring about new enhancements and functionalities. Unlike traditional firewalls that examine and restrict incoming packets based on predefined rules, distributed firewalls analyze each packet independently, leading to certain limitations. Ho","cbCaier1G8rvsw3V","https://ap.wps.com/l/cbCaier1G8rvsw3V","pdf",725790,11,1,12,"English","en",105,"# Abstract\n# Introduction\n## Cloud computing and virtualization in healthcare\n## Security challenges in cloud networks\n## Related work: distributed firewalls and IoT security","[{\"question\":\"What problem does divided-cloud aim to solve in cloud healthcare deployments?\",\"answer\":\"It addresses the lack of adaptability of traditional security mechanisms when network topology and VM resources change mid-execution, by organizing the cloud into zones with controllers monitoring VMs under firewall protection.\"},{\"question\":\"Which machine learning methods are used for controller placement and how accurate are they?\",\"answer\":\"Decision tree and linear discriminant analysis (LDA) are used to improve the accuracy of adding new controllers, reaching up to 89%.\"},{\"question\":\"How is the optimal placement of new virtual machines determined?\",\"answer\":\"A K-neighbours classifier (KNN) is used to find optimal locations for new VMs, achieving an accuracy of 83%.\"}]","Automating cloud virtual machines allocation via machine learning | 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problem does divided-cloud aim to solve in cloud healthcare deployments?","Question",{"text":77,"@type":78},"It addresses the lack of adaptability of traditional security mechanisms when network topology and VM resources change mid-execution, by organizing the cloud into zones with controllers monitoring VMs under firewall protection.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which machine learning methods are used for controller placement and how accurate are they?",{"text":82,"@type":78},"Decision tree and linear discriminant analysis (LDA) are used to improve the accuracy of adding new controllers, reaching up to 89%.",{"name":84,"@type":75,"acceptedAnswer":85},"How is the optimal placement of new virtual machines determined?",{"text":86,"@type":78},"A K-neighbours classifier (KNN) is used to find optimal locations for new VMs, achieving an accuracy of 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