Fisher: An Efficient Container Load Prediction Model with Deep Neural Network in Clouds

摘要

Recently, more and more applications have been deployed in container, and prediction of container load is essential in Clouds for improving resource utilization and achieving service-level agreements. However, accurate prediction of container load in Clouds remains an extremely challenge because the container load fluctuates drastically at small timescales. Furthermore, with many metrics of container in Clouds, it is hard to find which metrics are going to be useful. To address these challenges, we design an efficient container load prediction model named Fisher to improve the accuracy and efficiency of prediction. It mainly includes two modules: a metrics selection module and a neural network training module. We first selects relevant metrics by metrics selection module which is a novel algorithm for shape-based time-series clustering. Afterwards, we apply a powerful deep neural network model built with bidirectional long short-term memory to predict actual load one-step-ahead. We evaluate Fisher using a 30-day load trace from a data center with 500 containers. Experiments show that Fisher can reduce training metrics while maintaining prediction accuracy. More importantly, our model significantly improves prediction accuracy over 50% compared to other state-of-the-art methods based on auto regressive integrated moving-average and long short-term memory.