A server consolidation method with integrated deep learning predictor in local storage based clouds

Server consolidation is one of the critical techniques for energy-efficiency in cloud data centers. As it is often assumed that cloud service instances (eg, Amazon EC2 instances) utilize the shared storage only. In recent years, however, cloud service providers have been providing local storage for cloud users, since local storage can offer a better performance with identified price. However, these cloud instances usually contain much more data than shared storage cloud instances. Thus, in such local storage based cloud center, the migration cost can be really high and is in dire need of an efficient resource pre-allocation. If we can predict the resource demand in advance, the migration oscillation will be reduced to minify the migration cost. We have found that there are some related work about server consolidation based on forecasting. Unfortunately, their latest work did not consider the background of "local storage" as we mentioned above. At the same time, some research about local storage did not involve the prediction strategy, which plays a significant part in server consolidation. To address this issue, this paper proposes Losari, a consolidation method, which takes numeric forecasting and local storage architecture into consideration. Losari consolidates servers on the basis of the resource demand predicted value using a statistical learning method. We model the workload from real cloud production environment as a time series. Taking deep learning as a frame of reference, multiple deep belief networks integrated with ARIMA model was trained to study the feature of historical workload. The experimental results have showed that its average predicted error is only 10.7% in the short term, which is much lower than the most common model based on threshold (19.8%) on the same dataset. What is more, the results show that Losari not only simulates the true sequences in high accuracy but also scales the compute resource well, which demonstrated the validity of this integrated deep learning model.