Applying gated recurrent units pproaches for workload prediction

Resource scheduling is a key technology of cloud computing. In order to manage the resources in cloud efficiently, it is necessary to use workload prediction techniques for resource management. There are some workload's prediction algorithms for this problem, but they all have problems with accuracy and computational efficiency. In this paper, a new approach for Workload Prediction based on Gated Recurrent Units (GRUWP) was proposed. The approach uses a more reasonable workload model and more suitable neural network model for workload prediction, and it is capable to learn the temporal patterns and long range dependencies on large sequences of arbitrary length. Extensive experiments show that the approach can accurately predict the workload on the physical machine(PM) compared with other widely used workload prediction algorithms.