An Intelligent Swarm Based Prediction Approach For Predicting Cloud Computing User Resource Needs

Abstract Cloud computing aimed at offering elastic resource allocation on demand to cloud consumers. Building a cloud resource demand prediction model is a challenging task because cloud consumer’s demand changes over time. Most of the current works in cloud resource demand prediction study the problem from the cloud provider perspective. In this paper, we study the problem from the cloud consumer perspective with the aim to help the consumers to meet their resource needs, derive estimates for the required IT budget, select the best cloud providers, and get better pricing through the advanced reservation of required cloud resources. We develop a new Swarm Intelligence Based Prediction Approach (SIBPA) to predict with higher accuracy the resource needs of a cloud consumer in terms of CPU, memory, and disk storage utilization. The SIBPA is also able to predict the response time and throughput which in turn enables the cloud consumers to make a better scaling decision. It also takes into account the dynamic behavior of consumer requests in a long term period and the seasonal or/and trend patterns in time series. The SIBPA uses the Particle Swarm Optimization (PSO) approach for selecting the best features from the dataset and for estimating the parameters of the prediction algorithms. The experimental results reveal that the prediction accuracy of the SIBPA outperforms the current prediction models. In terms of CPU utilization prediction, the accuracy of SIBPA outperforms the accuracy of the existing cloud consumer prediction frameworks that use Linear Regression, Neural Network, and Support Vector Machines approaches by 56.95%, 80.42%, and 63.86% respectively according to RMSE, and by 72.66%, 44.24%, and 56.78% according to MAPE. The accuracy of SIBPA also outperforms the accuracy of the existing cloud consumer prediction frameworks in terms of response time, throughput, and memory utilization predictions. The analysis and experiment results of SIBPA are discussed in detail in this paper.

[1]  Kuan-Yu Chen,et al.  A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan , 2007, Expert Syst. Appl..

[2]  Ping-Feng Pai,et al.  A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .

[3]  G. Box,et al.  Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models , 1970 .

[4]  Victor I. Chang,et al.  The Business Intelligence as a Service in the Cloud , 2014, Future Gener. Comput. Syst..

[5]  Hui Wang,et al.  Diversity enhanced particle swarm optimization with neighborhood search , 2013, Inf. Sci..

[6]  Samuel A. Ajila,et al.  Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment , 2016 .

[7]  Najme Mansouri,et al.  Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory , 2019, Comput. Ind. Eng..

[8]  Victor I. Chang,et al.  Towards data analysis for weather cloud computing , 2017, Knowl. Based Syst..

[9]  K. K. Mishra,et al.  A Direction Aware Particle Swarm Optimization with Sensitive Swarm Leader , 2018, Big Data Res..

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

[11]  Ch. Aswani Kumar,et al.  Predictive Cloud resource management framework for enterprise workloads , 2016, J. King Saud Univ. Comput. Inf. Sci..

[12]  Lee-Ing Tong,et al.  Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming , 2011, Knowl. Based Syst..

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

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

[15]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[16]  Chung-Horng Lung,et al.  An autonomic prediction suite for cloud resource provisioning , 2017, Journal of Cloud Computing.

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  Christina Delimitrou,et al.  Quasar: resource-efficient and QoS-aware cluster management , 2014, ASPLOS.

[19]  Aluizio F. R. Araújo,et al.  A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case , 2003, Neural Computation.

[20]  Sarbani Roy,et al.  Resource requirement prediction using clone detection technique , 2013, Future Gener. Comput. Syst..

[21]  Mehdi Khashei,et al.  A novel hybrid classification model of artificial neural networks and multiple linear regression models , 2012, Expert Syst. Appl..

[22]  Ningjiang Chen,et al.  A User Preference and Service Time Mix-aware Resource Provisioning Strategy for Multi-tier Cloud Services , 2013 .

[23]  Victor Chang,et al.  IoT, big data and HPC based smart flood management framework , 2017, Sustain. Comput. Informatics Syst..

[24]  Haoyu Wang,et al.  A cloud server energy consumption measurement system for heterogeneous cloud environments , 2018, Inf. Sci..

[25]  Pei-Chann Chang,et al.  Iterated time series prediction with multiple support vector regression models , 2013, Neurocomputing.

[26]  Jianzhou Wang,et al.  Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling , 2010 .

[27]  B. V. V. S. Prasad,et al.  Predicting Future Resource Requirement for Efficient Resource Management in Cloud , 2014 .

[28]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.