  Cloud computing has been regarded as a successful paradigm for IT industry by
providing benefits for both service providers and customers. In spite of the
advantages, cloud computing also suffers from distinct challenges, and one of
them is the inefficient resource provisioning for dynamic workloads. Accurate
workload predictions for cloud computing can support efficient resource
provisioning and avoid resource wastage. However, due to the high-dimensional
and high-variable features of cloud workloads, it is difficult to predict the
workloads effectively and accurately. The current dominant work for cloud
workload prediction is based on regression approaches or recurrent neural
networks, which fail to capture the long-term variance of workloads. To address
the challenges and overcome the limitations of existing works, we proposed an
efficient supervised learning-based Deep Neural Network (esDNN}) approach for
cloud workload prediction. Firstly, we utilize a sliding window to convert the
multivariate data into supervised learning time series that allow deep learning
for processing. Then we apply a revised Gated Recurrent Unit (GRU) to achieve
accurate prediction. To show the effectiveness of esDNN, we also conduct
comprehensive experiments based on realistic traces derived from Alibaba and
Google cloud data centers. The experimental results demonstrate that esDNN can
accurately and efficiently predict cloud workloads. Compared with the
state-of-the-art baselines, esDNN can reduce the mean square errors
significantly, e.g. 15% than the approach using GRU only. We also apply esDNN
for machines auto-scaling, which illustrates that esDNN can reduce the number
of active hosts efficiently, thus the costs of service providers can be
optimized.
