Cloud computing has drastically transformed the means of computing in recent years. In spite of numerous benefits

it suffers from some challenges too. Major challenges of cloud computing include dynamic resource scaling and power consumption. These factors lead a cloud system to become inefficient and costly. The workload prediction is one of the variables by which the efficiency and operational cost of a cloud can be improved. Accuracy is the key component in workload prediction. On the basis of previous studies

this paper is presented a workload prediction model using neural network and adaptive differential evolution algorithm. The model is capable of learning the best suitable mutation strategy along with optimal crossover rate. Finally

we compared the results with prediction model based on back propagation learning algorithm and proves the effectiveness of the proposed model.