Applying gated recurrent units pproaches for workload prediction

Resource scheduling is a key technology of cloud computing. In order to manage the resources in cloud efficiently, it is necessary to use workload prediction techniques for resource management. There are some workload's prediction algorithms for this problem, but they all have problems with accuracy and computational efficiency. In this paper, a new approach for Workload Prediction based on Gated Recurrent Units (GRUWP) was proposed. The approach uses a more reasonable workload model and more suitable neural network model for workload prediction, and it is capable to learn the temporal patterns and long range dependencies on large sequences of arbitrary length. Extensive experiments show that the approach can accurately predict the workload on the physical machine(PM) compared with other widely used workload prediction algorithms.

[1]  Di Liu,et al.  An Improved Dynamic Load-Balancing Model , 2016, 2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD).

[2]  Jinjun Chen,et al.  CPU load prediction for cloud environment based on a dynamic ensemble model , 2014, Softw. Pract. Exp..

[3]  Ashraf A. Shahin.  Automatic Cloud Resource Scaling Algorithm based on Long Short-Term Memory Recurrent Neural Network , 2017, ArXiv.

[4]  Tanvir Ahmed,et al.  A theoretical study on classifier ensemble methods and its applications , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[7]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[8]  Rajkumar Buyya,et al.  Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS , 2015, IEEE Transactions on Cloud Computing.

[9]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[10]  Herbert Jaeger,et al.  A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the " echo state network " approach - Semantic Scholar , 2005 .

[11]  M. A. S. Monfared,et al.  A new adaptive exponential smoothing method for non-stationary time series with level shifts , 2014 .

[12]  Jing Wu,et al.  A Type-Aware Workload Prediction Strategy for Non-stationary Cloud Service , 2017, 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA).

[13]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[14]  S. A. Feyzbakhsh,et al.  Adam-Eve genetic algorithm as a function optimizer , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[15]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[16]  Kin Keung Lai,et al.  Hybridizing Exponential Smoothing and Neural Network for Financial Time Series Predication , 2006, International Conference on Computational Science.

[17]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.