Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

• Published in: IEEE transactions on parallel and distributed systems Vol. 31; no. 4; pp. 923 - 934
• Main Authors: Chen, Zheyi, Hu, Jia, Min, Geyong, Zomaya, Albert Y., El-Ghazawi, Tarek
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Coders; Data analysis; Data centers; Data mining; Deep learning; Feature extraction; Machine learning; Predictive models; Provisioning; Recurrent neural networks; Resource allocation; resource provisioning; sequential data analysis; Workload; workload prediction; Workloads
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2019.2953745

Resource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads. However, the existing methods cannot effectively predict the high-dimensional and highly-variable cloud workloads. This results in resource wasting and inability to satisfy service level agreements (SLAs). Since recurrent neural network (RNN) is naturally suitable for sequential data analysis, it has been recently used to tackle the problem of workload prediction. However, RNN often performs poorly on learning long-term memory dependencies, and thus cannot make the accurate prediction of workloads. To address these important challenges, we propose a deep Learning based Prediction Algorithm for cloud Workloads (L-PAW). First, a top-sparse auto-encoder (TSA) is designed to effectively extract the essential representations of workloads from the original high-dimensional workload data. Next, we integrate TSA and gated recurrent unit (GRU) block into RNN to achieve the adaptive and accurate prediction for highly-variable workloads. Using real-world workload traces from Google and Alibaba cloud data centers and the DUX-based cluster, extensive experiments are conducted to demonstrate the effectiveness and adaptability of the L-PAW for different types of workloads with various prediction lengths. Moreover, the performance results show that the L-PAW achieves superior prediction accuracy compared to the classic RNN-based and other workload prediction methods for high-dimensional and highly-variable real-world cloud workloads.