Resource finding is an enormous and tedious task in the cloud environment. There are various protocols suggested in existing approach to deal the resource finding. But from job scheduling to resource identification none of the approaches is available for best. It leads to a tailoring of functionalities in different phases like job scheduling, resource finding and resource identification. This paper analyses the automation from the job scheduling phase to resource allocation phase which includes, semantic model, optimization, Master Resource Finding Engine (MRFE). A decision-making mechanism called adjudicator is a control unit which collects input from all the above-said phase models and provides the best and efficient resource to the requirement. In this paper, a new model is provisioned to attain all the phases explained above and compared with resource simulations. The results were analyzed in terms of quality of resource, performance, resources acquired, patterns considered for semantic finding, interactive jobs, a workload of resources in normal and peak time. © 2020 Elsevier B.V., All rights reserved.