PD-GABP — A novel prediction model applying for elastic applications in distributed environment

In comparison with other scaling techniques, forecast of workload and resource consumption brings a great advantage to SaaS operations in cloud environment because system knows early and precisely the number of resources must be increased or decreased. However, the prediction accuracy still needs to be improved further even though there are many research works that have dealt with the problem. In this paper, we present a novel prediction model, which combines periodicity detection technique and neural network trained by genetic-back propagation algorithm to forecast the future values of time series data. The model is experimented with real workload dataset of a web application. The tests proved significant effectiveness of the model in improving the prediction accuracy. Our model thus can enhance the performance of applications running on cloud and distributed environment.

[1]  Ram Krishnan,et al.  Time Series Forecasting of Cloud Data Center Workloads for Dynamic Resource Provisioning , 2015, J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl..

[2]  Johan Tordsson,et al.  Workload Classification for Efficient Auto-Scaling of Cloud Resources , 2013 .

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

[4]  Philip S. Yu,et al.  On Periodicity Detection and Structural Periodic Similarity , 2005, SDM.

[5]  Aniruddha S. Gokhale,et al.  Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[6]  R. Saravanan,et al.  A genetic algorithm-based artificial neural network model for the optimization of machining processes , 2009, Neural Computing and Applications.

[7]  Walid G. Aref,et al.  Multiple and Partial Periodicity Mining in Time Series Databases , 2002, ECAI.

[8]  K. Hipel,et al.  Time series modelling of water resources and environmental systems , 1994 .

[9]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[10]  Jinhui Huang,et al.  Resource prediction based on double exponential smoothing in cloud computing , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[11]  Ladislav Hluchý,et al.  A Generic Development and Deployment Framework for Cloud Computing and Distributed Applications , 2013, Comput. Informatics.

[12]  Chris Chatfield,et al.  Time series forecasting with neural networks: a comparative study using the air line data , 2008 .

[13]  Binh Minh Nguyen,et al.  A Strategy for Server Management to Improve Cloud Service QoS , 2015, 2015 IEEE/ACM 19th International Symposium on Distributed Simulation and Real Time Applications (DS-RT).

[14]  Charles Wafula,et al.  SEASONAL TIME SERIES FORECASTING: A COMPARATIVE STUDY OF ARIMA AND ANN MODELS , 2006 .

[15]  Shifei Ding,et al.  An optimizing BP neural network algorithm based on genetic algorithm , 2011, Artificial Intelligence Review.

[16]  J. Faraway,et al.  Time series forecasting with neural networks: a comparative study using the air line data , 2008 .

[17]  Coskun Hamzaçebi,et al.  Improving artificial neural networks' performance in seasonal time series forecasting , 2008, Inf. Sci..