Cloud-based Task Scheduling in a Secure Data-Sharing Scheme using a Cloud Environment

Because of the dynamic nature of data dissemination in cloud computing, traditional database views in conventional architecture present several issues for data miners. This paper offers a methodology that applies the k-means approach to data mining in such cases. These days, one of the most essential issues is the need for new high-speed cloud services. Various load-balancing techniques, such as Secure Data Sharing Schemes (SDSS), Attribute-Based Encryption, and Byte-Cyclic Encryption (CP-ABE) algorithms, have already been considered effective ways to allocate requests in a cloud context. Cloud computing technology is introduced, and the mass information processing capacity reconfiguration of the distribution automation main station is proposed by leveraging its high dependability, dynamic resource adjustment, and other features. Non-preemptive task scheduling is irreversible; thus, it is started at the above virtual machine and allocated to the right ones. Various dependent activities that can finish self-governing tasks on different virtual machines and many cores on the same virtual machine are typically included in starting tasks.