Abstract

Cloud computing offers huge data processing with comfortable economy. In fact, it uses pay-per-use model that use like outsourcing of processing and storage equipment. Broadband and other network technologies make this idea into reality. On the other side, Cloud venders always try to use their resources in most efficient way that satisfy different customer and their heterogeneous requirements. As resources are always limited, Cloud venders multiplexed their resource among workloads. This switching can be performed by three main strategies including Artificial Intelligence, Predictive Resource Allocation and Dynamic Resource Allocation [1]. It is very clear that if cloud resource management system is enabled to predict the workload properly then Cloud system manage more efficiently. In following study, we develop and investigate, a model by using Naïve Bayes with Kernel Density Estimation. The evaluation of the model was impressive up 99.1% correct predictions.