Efficient resource identification and discovery is the primary requirements for cloud computing services, as it assists in scheduling and managing of cloud applications. Cloud computing is a revolution of the economic model rather than technological. It takes advantage of several technologies that were tested and modified by replacing the local use of computers with centralized shared resources that are managed and stored by Cloud Service Providers (CSPs) in a transparent manner for Cloud Consumers (CCs). With this new use, various cloud services have appeared and it is mainly classified into three broad categories i.e., Infrastructure as a service (IaaS), Software as a service (SaaS) and Platform as a service (PaaS). Each of these cloud services provides several benefits to the CCs through their respective Quality of Service (QoS) metric. Among the cloud service models, most of the QoS attribute and metric are identical and some are different. The vendors of cloud have focused their objectives on the development of scalability, resource consumption and performance, other characteristics of cloud have been ignored. While CSPs face challenging difficulties in publishing cloud services that displays their cloud resources, at the same time CCs do not have standard mechanism for cloud resource discovery, automated cloud services selection, and easy use of cloud services. In this frame, this paper puts forward a set of QoS metric for SaaS, IaaS, PaaS services and propose (i) An efficient algorithm for identifying the cloud services based on the QoS metric given by the cloud consumer using decision tree classification algorithm (ii) An efficient algorithm for Cloud service resource registry which aims to enable CSPs to register their services with its QoS attributes and (iii) A Cloud service resource discovery that search for the suitable cloud service and their attributes in the cloud service registry that meets the CCs application requirements using Split and Cache (SAC) algorithm. Our new approach makes the provisioning of cloud service possible by ease of resource identification, publication, discovery based on dynamic QoS attributes via web GUI interface backed by series of test that has validated and the proposed approach is feasible and sound. The recommended solution is important: instead of putting effort in locating, learning about the services and evaluating them, CCs can easily identify, discover the services, select and use the required cloud resources. The efficiency of our algorithms was assessed through experiments using CloudSim, which primarily decreases the response time, CPU utilization and memory consumption for identifying and searching the cloud services and increases the accuracy of the CSPs list retrieved along with their QoS attributes. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.