Cost effective cloud resource provisioning with imperialist competitive algorithm optimization

Cloud computing responds to user demands with providing computing and storage services. Most of cloud providers, suggest two payment plans to users, namely reservation and on-demand. In reservation plan users have to reserve resources and pay to cloud providers in advance despite the uncertainty in regards to demand. but in on-demand plan, users only pay in exchange for resource usage. Commonly, price of resources in on-demand plan is more expensive than that is in reservation plan. In this paper, we propose a cost effective cloud resource provisioning with imperialist competitive algorithm optimization (CECRPICAO). After predicting demand with a demand predictor algorithm, CECRPICAO allocate the virtual machines with reservation and on-demand plans using these predicted demands. The performance of proposed algorithm is evaluated by numerical studies and simulation. Results show that compared with several prior resource provisioning methods, CECRPICAO can provide the more affordable solution.

[1]  Bu-Sung Lee,et al.  Optimization of Resource Provisioning Cost in Cloud Computing , 2012, IEEE Transactions on Services Computing.

[2]  K.-Peter Holz,et al.  Short-term water level prediction using neural networks and neuro-fuzzy approach , 2003, Neurocomputing.

[3]  Mostafa Zandieh,et al.  A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties , 2011, Expert Syst. Appl..

[4]  Julien Gossa,et al.  Cost-Wait Trade-Offs in Client-Side Resource Provisioning with Elastic Clouds , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[5]  Chen-Khong Tham,et al.  Evolutionary Optimal Virtual Machine Placement and Demand Forecaster for Cloud Computing , 2011, 2011 IEEE International Conference on Advanced Information Networking and Applications.

[6]  Rajkumar Buyya,et al.  Cloud Computing Principles and Paradigms , 2011 .

[7]  Seoyoung Kim,et al.  A Science Cloud Resource Provisioning Model Using Statistical Analysis of Job History , 2011, 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing.

[8]  Neil Davey,et al.  Time Series Prediction and Neural Networks , 2001, J. Intell. Robotic Syst..

[9]  Siamak Talatahari,et al.  IMPERIALIST COMPETITIVE ALGORITHM FOR ENGINEERING DESIGN PROBLEMS , 2010 .

[10]  Zhiping Lin,et al.  Wavelet Packet Multi-layer Perceptron for Chaotic Time Series Prediction: Effects of Weight Initialization , 2001, International Conference on Computational Science.

[11]  Bu-Sung Lee,et al.  Optimal virtual machine placement across multiple cloud providers , 2009, 2009 IEEE Asia-Pacific Services Computing Conference (APSCC).

[12]  Rajkumar Buyya,et al.  QoS-aware Deployment of Network of Virtual Appliances Across Multiple Clouds , 2011, 2011 IEEE Third International Conference on Cloud Computing Technology and Science.

[13]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[14]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[15]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[16]  Yong Wang,et al.  Improved Imperialist Competitive Algorithm for Constrained Optimization , 2009, 2009 International Forum on Computer Science-Technology and Applications.