A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing

Nowadays, using cloud computing services is becoming very popular among corporations and dependent users because of its flexibility and diverse facilities. So, demand for its infrastructures is increasing exponentially and with this huge growth of demands, it is getting more challenging for cloud providers to serve all the user requests in a way that quality of service is high enough and service level agreement is also met. Along years, researchers found out that for an optimized resource allocation which provides required user quality of service it is needed to know the amount of future workload in advance so resources can be prepared. There are many methods introduced for cloud workload prediction but excessive changes in the number of requests in cloud workloads cause a reduction in their prediction accuracy. To solve this problem, we have proposed a novel hybrid wavelet neural network method which can efficiently model excessive changes in workload and predict the upcoming requests. For training the proposed method accurately, we have used two heuristic algorithms, Artificial Immune System and Water Cycle Algorithm. The two mentioned heuristic algorithms are used for finding optimized parameters (such as bias and weight) for the wavelet neural network algorithm. The proposed wavelet neural network trained with heuristic algorithms is tested on real and standard cloud workloads. At last, the accuracy of the proposed method is thoroughly surveyed statistically and also compared with other state-of-the-art prediction methods. Simulation results show that our method improves MAPE error at least 10% in comparison to other rival prediction methods.