This paper presents the Reinforced Dynamic Clustering (RDC) for optimal selection of cloud packages which enable effective package allocation for users. This model operates on four major phases. The initial phase identifies the QoS requirements of customers and clusters them effectively. The second phase identifies the average QoS requirements based on each of the clusters. Decision Tree model is used to train on the data from the clusters and to predict packages that are most suitable for each of the clusters. The next phase handles the real-time resource requirements from the users and allocates packages. The final phase aggregates the user requirements, which are then used in the clustering phase to incorporate the latest user requirements. Experiments were performed with the access log data and comparisons were performed with state-of-the-art models. Results indicate highly effective performances of the proposed model.

Resource provisioning, Cloud resource allocation, Clustering, Package Selection, Reinforcement

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