In Cloud environments, efficient resource provisioning and management present today a challenging issue because of the dynamic nature of the Cloud on one hand, and the need to satisfy heterogeneous resource requirements on the other hand. In such dynamic environments where end-users can arrive and leave the Cloud at any time, a Cloud service provider (CSP) should be able to make accurate decisions for scaling up or down its data-centers while taking into account several utility criteria, e.g., the delay of virtual resources setup, the migration of existing processes, the resource utilization, etc. In order to satisfy both parties (the CSP and the end-users), an efficient and dynamic resource allocation strategy is mandatory. In this paper, we propose an original approach for dynamic resource allocation in a Cloud environment. Our proposal considers computing job requests that are characterized by their arrival and teardown times, as well as a predictive profile of their computing requirements during their activity period. Assuming a prior knowledge of the predicted computing resources required by end-users, we propose and investigate several algorithms with different optimization criteria. However, prediction errors may occur resulting in some cases in the drop of one or several computing requests. Our proposed algorithms are compared in terms of various performance parameters including the rejection ratio, the dropping ratio, as well as the satisfaction of the endusers and the CSP.