  Cloud services have grown rapidly in recent years, which provide high
flexibility for cloud users to fulfill their computing requirements on demand.
To wisely allocate computing resources in the cloud, it is inevitably important
for cloud service providers to be aware of the potential utilization of various
resources in the future. This paper focuses on predicting CPU utilization of
virtual machines (VMs) in the cloud. We conduct empirical analysis on Microsoft
Azure's VM workloads and identify important conceptual characteristics of CPU
utilization among VMs, including locality, periodicity and tendency. We propose
a neural network method, named Time-aware Residual Networks (T-ResNet), to
model the observed conceptual characteristics with expanded network depth for
CPU utilization prediction. We conduct extensive experiments to evaluate the
effectiveness of our proposed method and the results show that T-ResNet
consistently outperforms baseline approaches in various metrics including RMSE,
MAE and MAPE.
