Abstract
Cloud systems are tools and software for cloud computing that are deployed on the Internet or a cloud computing network, and users can use them at any time. After assessing and choosing cloud providers, however, customers confront the variety and difficulty of quality of service (QoS). To increase customer retention and engagement success rates, it is critical to research and develops an accurate and objective evaluation model. Cloud is the emerging environment for distributed services at various layers. Due to the benefits of this environment, globally cloud is being taken as a standard environment for individuals as well as for the corporate sector as it reduces capital expenditure and provides secure, accessible, and manageable services to all stakeholders but Cloud computing has security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Allocation of resources in the Cloud is difficult because resources provide numerous measures of quality of service. In this paper, the proposed resource allocation approach is based on attribute QoS Scoring that takes into account parameters the reputation of the asset, task completion time, task completion ratio, and resource loading. This article is focused on the cloud service's security, cloud reliability, and could performance. In this paper, the machine learning algorithm neuro-fuzzy has been used to address the cloud security issues to measure the parameter security and privacy, trust issues. The findings reveal that the ANFIS-dependent parameters are primarily designed to discern anomalies in cloud security and features output normally yields better results and guarantees data consistency and computational power.