  With the rapid growth of the data volume and the fast increasing of the
computational model complexity in the scenario of cloud computing, it becomes
an important topic that how to handle users' requests by scheduling
computational jobs and assigning the resources in data center.
  In order to have a better perception of the computing jobs and their requests
of resources, we analyze its characteristics and focus on the prediction and
classification of the computing jobs with some machine learning approaches.
Specifically, we apply LSTM neural network to predict the arrival of the jobs
and the aggregated requests for computing resources. Then we evaluate it on
Google Cluster dataset and it shows that the accuracy has been improved
compared to the current existing methods. Additionally, to have a better
understanding of the computing jobs, we use an unsupervised hierarchical
clustering algorithm, BIRCH, to make classification and get some
interpretability of our results in the computing centers.
