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Cloud computing is a popular technology that offers clients various services remotely daily. Nowadays, cloud computing is becoming a fast-growing industry that contains the potential to provide extensible services via the internet using software and hardware virtualization. It has become difficult for cloud companies to manage the enormous volume of data and diverse resources in the cloud due to its rapid growth. Most clients range from individual users to large corporations or business houses. To prevent over-provisioning and under-provisioning problems, it is essential to ensure the availability of the resources to the end user with the least amount of administration. As a result, resources must be equitably distributed among various stakeholders without sacrificing the organization’s profitability or customer satisfaction. The enormous load from dynamic sources with various formats makes resource allocation and scheduling a complex process.It also leads to several security issues, which include privacy, interoperability, credibility, following critical standards, and collaborations become a significant concern in cloud computing. We proposed cloud service provider dynamic scheduling (CSPDS) to address these issues. The proposed system is efficiently designed to handle various formats and huge loads in the cloud environment. The cloud service provider (CSP) plays a primary role in our proposed methodology (CSPDS), which controls and monitors the entire network. The CSPDS enhances the network’s QoS (quality of service) in resource monitoring, resource utilization, and resource allocation. Next, the migration model provides the best resource utilization, and the cryptography modules improve the users’ overall data security in the cloud. Finally, in the experimental section, a comparison work is conducted between the proposed CSPDS with existing resource allocation& task scheduling using a hybrid machine learning (RATS-HM) 13 technique and Resource Allocation Algorithm (RAA)14. In the comparison work, these algorithms are analyzed with evaluation metrics such as utilization, system efficiency, migration time, and security performance. The obtained results state in all the metrics that the proposed CSPDS performance is more efficient than the others.
Prathapkumar et al. (Fri,) studied this question.