Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds
DOI:
https://doi.org/10.25126/jitecs.202162196Abstract
The main cause of energy wastage in cloud data centres is the low level of server utilization. Low server utilization is a consequence of allocating more resources than required for running applications. For instance, in Infrastructure as a Service (IaaS) public clouds, cloud service providers (CSPs) deliver computing resources in the form of virtual machines (VMs) templates, which the cloud users have to choose from. More often, inexperienced cloud users tend to choose bigger VMs than their application requirements. To address the problem of inefficient resources utilization, the existing approaches focus on VM allocation and migration, which only leads to physical machine (PM) level optimization. Other approaches use horizontal auto-scaling, which is not a visible solution in the case of IaaS public cloud. In this paper, we propose an approach of customizing user VM’s size to match the resources requirements of their application workloads based on an analysis of real backend traces collected from a VM in a production data centre. In this approach, a VM is given fixed size resources that match applications workload demands and any demand that exceeds the fixed resource allocation is predicted and handled through vertical VM auto-scaling. In this approach, energy consumption by PMs is reduced through efficient resource utilization. Experimental results obtained from a simulation on CloudSim Plus using GWA-T-13 Materna real backend traces shows that data center energy consumption can be reduced via efficient resource utilizationReferences
P. Jemishkumar, I.-L. Y. Vasu, B. Farokh, Jindal, X. Jie and G. Peter, "Workload Estimation for Improving Resource Management Decisions in the Cloud.," in 2015 IEEE TwelfthInternational Symposium on Autonomous Decentralized Systems, 2015.
I. 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.
G. Chaima, "Energy efficient resource allocation in cloud computing Enviroment," Institut National des T´el´ecommunications, Paris, France , 2014.
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 centre Resources," The Computer Journal, 2015.
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.
VMware, "Performance Best Practices for VMware vSphere 6.0," VMware, Inc, Palo Alto, CA, 2015.
D. Kenga, V. Omwenga and P. Ogao, "Statistical Techniques for Characterizing Cloud Workloads: A Survey," in 4th Strathmore International Mathematics Conference, Nairobi, 2017.
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].
Amazon, "ParkMyCloud Cost Optimization, Scheduler & Management Deprecated," Amazon, 2019. [Online]. Available: https://aws.amazon.com/marketplace/pp/B07K2L9YZW. [Accessed 10 January 2019].
Amazon Web Services, "Right Sizing: Provisioning Instances to Match Workloads: AWS Whitepaper," Amazon Web Services, Inc., 2018.
ParkMyCloud, "Why Azure Right Sizing is Important," ParkMyCloud, 2018. [Online]. Available: https://www.parkmycloud.com/azure-right-sizing/. [Accessed 01 November 2018].
L. Yazdanov, "TOWARDS AUTO-SCALING IN THE CLOUD: ONLINE RESOURCE ALLOCATION TECHNIQUES," Technische Universitat Braunschweig, 2016.
N. Krishnaveni and G. Sivakumar, "Survey on Dynamic Resource Allocation Strategy in Cloud Computing Environment," International Journal of Computer Applications Technology and Research, vol. 2, no. 6, pp. 731 - 737, 2013.
X. Zhanga, T. Wu, M. Chen, T. Wei, J. Zhou, S. Hu and R. Buyya, "Energy-aware virtual machine allocation for cloud with resource reservation," The Journal of Systems and Software, vol. 147, no. 2019, pp. 147-161, 2019.
S. Kaur and V. Pandey, "A Survey of Virtual Machine Migration Techniques in Cloud Computing," Computer Engineering and Intelligent Systems , vol. 6, no. 7, 2015.
F. P. Sareh, "Energy-Efficient Management of Resources in Enterprise and Container-based Clouds," The University of Melbourne, 2016.
O. A. Ben-Yehuda, M. Ben-Yehuda, A. Schuster and D. Tsafrir, "The rise of RaaS: the resource-as-a-service cloud," Communications of the ACM, vol. 57, no. 7, pp. 76-84, 2014.
A. 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.
V. Emeakaroha, M. Netto, R. Calheiro, I. Brandic, R. Buyya and C. Rose, "Towards autonomic detection of SLA violations in Cloud infrastructures," Future Generation Computer Systems, vol. 28, no. 7, pp. 1017-1029, 2012.
M. Alam, A. S. Kashish and S. Shuchi, "Analysis and Clustering of Workload in Google Cluster Trace Based on Resource Usage," in 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES), Paris, France, 2016.
S. Shen, V. v. Beek and A. Iosup, "Statistical Characterization of Business-Critical Workloads Hosted in Cloud Data centres," in 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Shenzhen, China, 2015.
Yahoo, "Computing Systems Data," Yahoo, 2019. [Online]. Available: https://webscope.sandbox.yahoo.com/catalog.php?datatype=s&guccounter=1&guce_referrer=aHR0cHM6Ly93ZWJzY29wZS5zYW5kYm94LnlhaG9vLmNvbS8&guce_referrer_sig=AQAAAC_THkiHAd-3c25yQ-faDODXLIKkWaUwVtxotuRxLvHDGn3mxbcWBQm9XEiH9rMjByu7Cfs-KbZ1p5JqKI1tK9rC0c5PTiiKaVRz. [Accessed 12 January 2019].
C. Reiss and J. Wilkes, "Google cluster-usage traces: format + schema," Google , 2011.
Delft University of Technology, "The Grid Workloads Archive," Delft University of Technology, 2019. [Online]. Available: http://gwa.ewi.tudelft.nl/. [Accessed January 10 2019].
Delf University of Technology, "GWA-T13-materna-trace," Delf University of Technology, 2018. [Online]. Available: http://gwa.ewi.tudelft.nl/datasets/gwa-t-13-materna. [Accessed 23 November 2018].
F. Manoel, R. Oliveira, C. Monteiro, P. Inácio and M. Freire, "CloudSim Plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness," in 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, Portugal, 2017.
C. Rodrigo, R. Rajiv, B. Anton, D. R. Cesar and B. Rajkumar, "CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms," Journal of Software: Practise and Experience , vol. 4, no. 1, pp. 23-50, 2011.
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: Hindawi, vol. 2017, no. 4873459, 2017.
G. Hadi and P. Massoud, "Achieving Energy Efficiency in Data centres by Virtual Machine Sizing, Replication, and Placement," in Energy Efficiency in Data centres and Clouds, Elsevier Science, 2016.
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.
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.
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.
S. Taherizadeh and V. Stankovski, "Dynamic Multi-level Auto-scaling Rules for Containerized Applications," The Computer Journal, vol. 62, no. 2, p. 174–197, 2019.
A. Shahin, "Automatic Cloud Resource Scaling Algorithm based on Long Short-Term Memory Recurrent Neural Network," International Journal of Advanced Computer Science and Applications, vol. 7, no. 12, pp. 279-285, 2016.
K. Derdus, V. Omwenga and P. Ogao, "Virtual Machine Sizing in Virtualized Public Cloud Data Centres," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 5, no. 4, 2019.
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