  Modern IT system operation demands the integration of system software and
hardware metrics. As a result, it generates a massive amount of data, which can
be potentially used to make data-driven operational decisions. In the basic
form, the decision model needs to monitor a large set of machine data, such as
CPU utilization, allocated memory, disk and network latency, and predicts the
system metrics to prevent performance degradation. Nevertheless, building an
effective prediction model in this scenario is rather challenging as the model
has to accurately capture the long-range coupling dependency in the
Multivariate Time-Series (MTS). Moreover, this model needs to have low
computational complexity and can scale efficiently to the dimension of data
available. In this paper, we propose a highly efficient model named HigeNet to
predict the long-time sequence time series. We have deployed the HigeNet on
production in the D-matrix platform. We also provide offline evaluations on
several publicly available datasets as well as one online dataset to
demonstrate the model's efficacy. The extensive experiments show that training
time, resource usage and accuracy of the model are found to be significantly
better than five state-of-the-art competing models.
