Derdus M. Kenga; Vincent O. Omwenga; Patrick J. Ogao

Autonomous Virtual Machine Sizing and Resource Usage Prediction for Efficient Resource Utilization in Multi-Tenant Public Cloud

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Author(s)

Derdus M. Kenga ^1,* Vincent O. Omwenga ^1,2 Patrick J. Ogao ²

1. Faculty of Information Technology, Strathmore University, Kenya

2. Faculty of Engineering Science and Technology, Technical University of Kenya, Kenya

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2019.05.02

Received: 16 Jan. 2019 / Revised: 10 Feb. 2019 / Accepted: 17 Feb. 2019 / Published: 8 May 2019

Index Terms

Cloud computing, virtual machine sizing, IaaS cloud, multi-tenant public cloud, energy efficiency, CloudSim plus, neural networks

Abstract

In recent years, the use of cloud computing has increased exponentially to satisfy computing needs in both big and small organizations. However, the high amounts of power consumed by cloud data centres have raised concern. A major cause of power wastage in cloud computing is inefficient utilization of computing resources. In Infrastructure as a Service, the inefficiency is caused when users request for more resources for virtual machines than is required. In this paper, we propose a technique for automatic virtual machine sizing and resource usage prediction using neural networks, for multi-tenant Infrastructure as a Service cloud service model. The proposed technique aims at reducing energy wastage in data centres by efficient resource utilization. An evaluation of our technique on CloudSim Plus cloud simulator and WEKA shows that effective VM sizing not only achieves energy savings but also reduces the cost of using cloud services from a customer perspective.

Cite This Paper

Derdus M. Kenga, Vincent O. Omwenga, Patrick J. Ogao, "Autonomous Virtual Machine Sizing and Resource Usage Prediction for Efficient Resource Utilization in Multi-Tenant Public Cloud", International Journal of Information Technology and Computer Science(IJITCS), Vol.11, No.5, pp.11-22, 2019. DOI:10.5815/ijitcs.2019.05.02

Reference

[1]Salam, R. Karim and M. Ali, "Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres," Journal of Cloud ComputingAdvances, Systems and Applications.

[2]G. Albert, H. James, A. M. David and P. Parveen, "The cost of a cloud: research problems in data centre networks," The ACM Digital Library is published by the Association for Computing Machinery, vol. 39, no. 1, 2009.

[3]Khosravi, "Energy and Carbon-Efficient Resource Management in Geographically Distributed Cloud Data Centers," The University of Melbourne, Melbourne, Australia, 2017.

[4]F. P. Sareh, "Energy-Efficient Management of Resources in Enterprise and Container-based Clouds," The University of Melbourne, 2016.

[5]J. Patel, V. Jindal, I.-L. Yen, F. Bastani, J. Xu and P. Garraghan, "Workload Estimation for Improving Resource Management Decisions in the Cloud," in 2015 IEEE Twelfth International Symposium on Autonomous Decentralized Systems, Taichung, Taiwan, 2015.

[6]B. Anton, "Energy-Efficient Management of Virtual Machines Data Centers for Cloud Computing," The University of Melbourne, 2013.

[7]M. Dabbagh, B. Hamdaoui, M. Guizani and A. Rayes, "Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment," IEEE Network, vol. 29, no. 2, 2015.

[8]G. Hadi and P. Massoud, "Achieving Energy Efficiency in Datacenters by Virtual Machine Sizing, Replication, and Placement," in Energy Efficiency in Data Centers and Clouds, Elsevier Science, 2016.

[9]R. Neha and J. Rishabh, "Cloud Computing: Architecture and Concept of Virtualization," International Journal of Science, Technology & Management, vol. 4, no. 1, 2015.

[10]B. Carmody, "Infrastructure On Demand Is Giving Small Businesses An Edge," Inc, 2018. [Online]. Available: https://www.inc.com/bill-carmody/infrastructure-on-demand-is-giving-small-businesses-an-edge.html. [Accessed 01 OCtober 2018].

[11]F. P. Sareh, R. N. Calheiros, J. Chan, A. V. Dastjerdi and R. Buyya, "Virtual Machine Customization and Task Mapping Architecture for Efficient Allocation of Cloud Data Center Resources," The Computer Journal, 2015.

[12]R. Hu, J. Jiang, G. Liu and L. Wang, "Efficient Resources Provisioning Based on Load Forecasting in Cloud," The Scientific World Journal, vol. 2014, no. 321231, 2014.

[13]D. Jiaqing, S. Nipun and Z. Willy, "Performance profiling in a virtualized environment," in HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing, Boston, USA, 2010.

[14]ParkMyCloud, "Why Azure Right Sizing is Important," ParkMyCloud, 2018. [Online]. Available: https://www.parkmycloud.com/azure-right-sizing/. [Accessed 01 November 2018].

[15]Google, "Applying Sizing Recommendations for VM Instances," Google, 2018. [Online]. Available: https://cloud.google.com/compute/docs/instances/apply-sizing-recommendations-for-instances. [Accessed 1 November 2018].

[16]Amazon Web Services, "Right-Sizing: Provisioning Instances to Match Workloads: AWS Whitepaper," Amazon Web Services, Inc., 2018.

[17]V. Patel and H. Bheda, "Reducing Energy Consumption with Dvfs for Real-Time Services in Cloud Computing," IOSR Journal of Computer Engineering (IOSR-JCE), vol. 16, no. 3, pp. 53-57, 2014.

[18]VMware, "vSphere Resource Management," VMware, Inc, Palo Alto, CA, 2015.

[19]X. Bronson, R. P. and S. S. Raja, "A Dynamic Memory Allocation Strategy for Virtual Machines in Cloud Platform," international Journal of Pure and Applied Mathematics, vol. 119, no. 15, pp. 1423-1444, 2018.

[20]S. Perera, "Multi-tenancy after 10 years of Cloud Computing," Hackernoon, 2016. [Online]. Available: https://hackernoon.com/multi-tenancy-after-10-years-of-cloud-computing-19de782ef899. [Accessed 01 November 2018].

[21]VMware, "Performance Best Practices for VMware vSphere 6.0," VMware, Inc, Palo Alto, CA, 2015.

[22]S. Shen, V. v. Beek and A. Iosup, "Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters," in 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Shenzhen, China, 2015.

[23]P. Xuesong, P. Barbara and V. Monica, "Virtual Machine Profiling for Analyzing Resource Usage of Applications," in International Conference on Services Computing, Milano, Italy, 2018.

[24]S. K. Tesfatsion, "Energy-efficient cloud computing: Autonomic resource provisioning for datacenters," Umea University, Umea, 2018.

[25]S. S. David and A. R., "Autonomic Resource Provisioning Algorithm for Cloud Computing using Match Making Technique," International Journal of Advanced Research in Computer Science and Software Engineering , vol. 6, no. 9, pp. 168 -173, 2016.

[26]G. ̈. Urul, "ENERGY EFFICIENT DYNAMIC VIRTUAL MACHINE ALLOCATION WITH CPU USAGE PREDICTION IN CLOUD DATACENTERS," Bilkent University, 2018.

[27]Delf University, "The Grid Workloads Datasets," Delf University, 2018. [Online]. Available: http://gwa.ewi.tudelft.nl/datasets/. [Accessed October 2 2018].

[28]M. Amiri and L. Mohammad-Khanli, "Survey on prediction models of applications for resources provisioning in cloud," Journal of Network and Computer Applications, vol. 82, 2017.

[29]Q. Z. Ullah, S. Hassan and G. M. Khan, "Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud," Journal of Computational Intelligence and Neuroscience: Hidawi, vol. 2017, 2017.

[30]M. Chen, H. Zhang, Y.-Y. Su, X. Wang, G. Jiang and K. Yoshihira, "Effective VM sizing in virtualized data centres," in 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops, Dublin, Ireland , 2011.

[31]R. Hu, G. Liu, J. Jiang and L. Wang, "A New Resources Provisioning Method Based on QoS Differentiation and VM Resizing in IaaS," Journal of Mathematical Problems in Engineering - Hidawi, vol. 2015, no. 215147, 2015.

[32]M. Aldossary and K. Djemame, "Performance and Energy-based Cost Prediction of Virtual Machines Live Migration in Clouds," in Proceedings of the 8th International Conference on Cloud Computing and Services Science. 8th International Conference on Cloud Computing and Services Science, Madeira, Portugal, 2018.

[33]R. N. Calheiros, E. Masoumi, R. Ranjan and R. Buyya, "Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS," IEEE TRANSACTIONS ON CLOUD COMPUTING, vol. 3, no. 3, pp. 449-458, 2016.

[34]J. Xue, F. Yan, R. Birke, L. Chen, T. Scherer and E. Smirni, "PRACTISE: Robust prediction of data centre time series," in 2015 11th International Conference on Network and Service Management (CNSM), Barcelona, Spain, 2015.

[35]Mozo, B. Ordozgoiti and S. Gómez-Canaval, "Forecasting short-term data center network traffic load with convolutional neural networks," PLOS one, 2018.

[36]J. J. Prevost, K. Nagothu, B. Kelley and M. Jamshidi, "Prediction of Cloud Data Center Networks Loads Using Stochastic and Neural Models," in Proceeding of the 2011 6th International Conference on System of Systems Engineering, Albuquerque, New Mexico, USA, 2011.

[37]S. Frey, S. Disch, C. Reich, M. Knahl and N. Clarke, "Cloud Storage Prediction with Neural Networks," in The Sixth International Conference on Cloud Computing, GRIDs, and Virtualization, 2015.

[38]M. Duggan, K. Mason, J. Duggan, E. Howley and E. Barrett, "Predicting Host CPU Utilization in Cloud Computing using Recurrent Neural Networks," in The 8th International Workshop on Cloud Applications and Security, 2017.

[39]H. Xu, X. Zuo, C. Liu and X. Zhao, "Predicting Virtual Machine’s Power via a RBF Neural Network," in International Conference in Swarm Intelligence, Bali, Indonesia, 2016.

[40]Y. Lu, J. Panneerselvam, L. Liu and Y. Wu, "RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing," Scientific Programming: Hidawi, vol. 2016, no. 5635673, 2016.

[41]R. Cao, Z. Yu, T. Marbach, J. Li, G. Wang and X. Liu, "Load Prediction for Data Centers Based on Database Service," in 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan, 2018.

[42]Google, "Machine types: Google compute documentation.," Google, 2018. [Online]. Available: https://cloud.google.com/compute/docs/machine-types. [Accessed November 02 2018].

[43]C. Ribeiro, M. Castro, M.-M. Vania and J.-F. Méhaut, "Evaluating CPU and Memory Affinity for Numerical Scientific Multithreaded Benchmarks on Multi-cores," IADIS International Journal on Computer Science and Information Systems, vol. 7, no. 1, pp. 79-93, 2012.

[44]J. M. Szefer, "Architectures for Secure Cloud Computing Servers," Princeton University, 2013.

[45]W. Kim, M. S. Gupta, G.-Y. Wei and D. Brooks, "System level analysis of fast, per-core DVFS using on-chip switching regulators," in 2008 IEEE 14th International Symposium on High Performance Computer Architecture, Salt Lake City, UT, USA, 2018.

[46]C. M. Kamga, "CPU frequency emulation based on DVFS," in 2012 IEEE Fifth International Conference on Utility and Cloud Computing, Chicago, IL, USA, 2013.

[47]X. Meng, X. Meng, X. Meng, X. Meng, X. Meng and X. Meng, "Efficient resource provisioning in compute clouds via VM multiplexing," in Proceedings of the 7th international conference on Autonomic computing , Washington DC, USA , 2010.

[48]G. Shmueli, R. P. Nitin, C. B. Peter, I. Yahav and C. L. Kenneth, "Neural nets," in Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, Wiley, 2017, p. 271.

[49]M. Khashei and M. Bijari, "An artificial neural network (p, d, q) model for timeseries forecasting," Expert Systems with Applications: Elsevier, vol. 37, no. 1, pp. 479-489, 2010.

[50]Waikato University, "Machine Learning at Waikato University," Waikato University, 2018. [Online]. Available: https://www.cs.waikato.ac.nz/ml/index.html. [Accessed 25 November 2018].

[51]S. Karsoliya, "Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture," International Journal of Engineering Trends and Technology, vol. 3, no. 6, pp. 714-717, 2012.

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