Machine Learning Based Prediction and Classification of Computational Jobs in Cloud Computing Centers
This work addresses resource management challenges in cloud computing centers, but it is incremental as it applies existing machine learning methods to a known dataset.
The paper tackles the problem of predicting and classifying computational jobs in cloud computing centers to improve resource scheduling, using LSTM for prediction and BIRCH for clustering, and reports improved accuracy on the Google Cluster dataset.
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.