Secured Dynamic Request Scheduling and Optimal CSP Selection for Analyzing Cloud Service Performance Using Intelligent Approaches

Due to its heterogeneity, cloud services providers’ (CSPs) rapid expansion presents several challenges, such as optimal service selection and privacy preservation. Multiple users using the cloud service at once increases the delay for service selection and request. Service interruptions result from centralized provisioning and insecurity. Existing work constraints include access control loss, service disruptions, security issues, trust management issues, and delays. Blockchain-based request scheduling and optimal CSP selection in edge-assisted clouds were presented in this research. Five phases—Data User (DS) authentication, sensitivity-aware request scheduling, policy verification, trust management, and optimal CSP selection—are proposed. In the first phase, DU authentication detects and eliminates authorized users. We suggested a chaotic map-based camellia encryption algorithm (CMCE) to boost security. The gateway schedules service requests using Johnson’s rule-based Stochastic Gradient Descent method, considering delay, throughput, and priority, in the second phase. This schedules the request into sensitive and non-sensitive services. Policy verification is done in the third phase utilizing Dynamic Policy-based Access control, which allows only sensitive requests. In phase four, we calculate the CSP trust value to boost security. Based on CSP behavior, we introduced the Multi Behavior Analysis-based Nomadic People Optimizer method. Every CSP’s trust value is modified based on user feedback over time. Finally, the best CSP is chosen for data user service, and suggested Dynamic and non-cooperative Game Theory is to choose the best CSP from a list. CloudSim is used to simulate and assess.