International Journal of Distributed and Cloud Computing

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Amazon EC2 Locations for Spot Pricing - A Hierarchical Clustering Approach


Affiliations
1 Department of Computer Science & Engineering, SRM Institute of Science and Technology, India
2 Manager, KPMG Services Pte. Ltd.,, Singapore
     

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EC2 provides cloud services through several regions and availability zones. Spot instances are offered by Amazon EC2 through spot pricing. Amazon launched it simplified spot pricing model at Reinvent: 2017. The prices change less frequently now and are more predictable. To ensure availability, spot instances can be acquired from diversified locations. The biggest concern to the user is the identification of regions and availability zones to choose for optimizing cost and maximizing availability. The study attempts to find similarity in cost per instance among different EC2 locations according to Ward’s Hierarchical Clustering Algorithm. The analysis uses past 60 days history traces from last week of September to November 2019 of four different compute instance types across all Amazon EC2 regions and availability zones. Results suggest a significant amount of similarity in terms of cost of instance in spot pricing across different locations. This raises user’s confidence in adopting spot market.

Keywords

Amazon EC2 regions, Availability zones, Hierarchical clustering, Spot instances.
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  • K. Veena, C. Anand, and G. C. Prakash, “Temporal and spatial trend analysis of cloud spot instance pricing in Amazon EC2,” 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/ DataCom/CyberSciTech), Aug. 2016.

  • R. Pary, New Amazon EC2 Spot Pricing Model: Simplified Purchasing without Bidding and Fewer Interruptions | Amazon Web Services. Accessed: Jan.16, 2020. [Online]. Available: https://aws.amazon.com/ blogs/compute/new-amazon-ec2-spot-pricing/

  • Docs.aws.amazon.com, Spot Instance Interruptions - Amazon Elastic Compute Cloud. Accessed: Jan. 26, 2020. [Online]. Available: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-interruptions. html

  • G. George, R. Wolski, C. Krintz, and J. Brevik, “Analyzing AWS spot instance pricing,” 2019 IEEE International Conference on Cloud Engineering (IC2E), Jun. 2019.

  • O. A. Ben-Yehuda, M. Ben-Yehuda, A. Schuster, and D. Tsafrir, “Deconstructing Amazon EC2 spot instance pricing,” ACM Transactions on Economics and Computation, vol. 1, no. 3, pp. 1-20, Sep. 2013.

  • N. Chohan, C. Castillo, M. Spreitzer, M. Steinder, A. N. Tantawi, and C. Krintz, “See spot run: Using spot instances for mapreduce workflows,” HotCloud. 2010, pp. 7-7.

  • A. Andrzejak, D. Kondo, and S. Yi, “Decision model for cloud computing under SLA constraints,” 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, Aug. 2010

  • M. Mazzucco, and M. Dumas, “Achieving performance and availability guarantees with spot instances,” 2011 IEEE International Conference on High Performance Computing and Communications, Sep. 2011

  • M. Zafer, Y. Song, and K.-W. Lee, “Optimal bids for spot VMs in a cloud for deadline constrained jobs,” 2012 IEEE 5th International Conference on Cloud Computing, Jun. 2012.

  • H. Zhao, M. Pan, X. Liu, X. Li, and Y. Fang, “Optimal resource rental planning for elastic applications in cloud market,” 2012 IEEE 26th International Parallel and Distributed Processing Symposium, May 2012.

  • R. M. Wallace, V. Turchenko, M. Sheikhalishahi, I.Turchenko, V. Shults, J. L. Vazquez-Poletti, and L. Grandinetti, “Applications of neural-based spot market prediction for cloud computing,” 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), Sep.2013.

  • B. Javadi, R. K. Thulasiram, and R. Buyya, “Characterizing spot price dynamics in public cloud environments,” Future Generation Computer Systems, vol. 29, no. 4, pp. 988-999, Jun. 2013.

  • V. K. Singh, and K. Dutta, “Dynamic price prediction for amazon spot instances,” 2015 48th Hawaii International Conference on System Sciences, Jan. 2015.

  • J. L. Lucas-Simarro, R. Moreno-Vozmediano, R. S. Montero, and I. M. Llorente, “Cost optimization of virtual infrastructures in dynamic multi-cloud scenarios,” Concurrency and Computation: Practice and Experience, vol. 27, no. 9, pp. 2260-2277, Dec. 2012.

  • V. Khandelwal, A. K. Chaturvedi, and C. P. Gupta, “Amazon EC2 spot price prediction using regression random forests,” IEEE Transactions on Cloud Computing, vol. 8, no. 1, pp. 59-72, Jan. 2020.

  • N. Ekwe-Ekwe, and A. Barker, “Location, location, location: Exploring Amazon EC2 spot instance pricing across geographical regions,” 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), May 2018.

  • M. Baughman, S. Caton, C. Haas, R. Chard, R. Wolski, I. Foster, and K. Chard, “Deconstructing the 2017 changes to AWS spot market pricing,” Proceedings of the 10th Workshop on Scientific Cloud Computing ScienceCloud’19, 2019.

  • Z. Gou, S. Yamaguchi, and B. B. Gupta, “Analysis of various security issues and challenges in cloud computing environment,” Identity Theft, pp. 221-247.

  • A. M. Manasrah, A. Aldomi, and B. B. Gupta, “An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment,” Cluster Computing, vol. 22, no. S1, pp.

  • -1653, Dec. 2017.

  • K. E. Psannis, C. Stergiou, and B. B. Gupta, “Advanced media-based smart big data on intelligent cloud systems,” IEEE Transactions on Sustainable Computing, vol. 4, no. 1, pp. 77-87, Jan. 2019.

  • G. Gao, L. Wu, and Y. Yan, “A secure storage scheme with key-updating in hybrid cloud,” International Journal of High Performance Computing and Networking, vol. 13, no. 2, p. 175, 2019.

  • C. Stergiou, K. E. Psannis, B. B. Gupta, and Y. Ishibashi, “Security, privacy & efficiency of sustainable cloud computing for big data & IoT,” Sustainable Computing: Informatics and Systems, vol. 19, pp. 174-184, Sep. 2018.

  • F. Murtagh, and P. Contreras, “Algorithms for hierarchical clustering: An overview,” WIREs Data Mining and Knowledge Discovery, vol. 2, no. 1, pp. 86-97, Dec. 2011.

  • R. J. Jensen, G. Dunn, and B. S. Everitt, “An introduction to mathematical taxonomy,” Systematic Botany, vol. 8, no. 1, p. 103, Jan. 1983.

  • D. P. Berrar, W. Dubitzky, and M. Granzow (Eds.), “A practical approach to microarray data analysis,” 2003.

  • M. R. Anderberg, “Conceptual problems in cluster analysis,” Cluster Analysis for Applications, pp. 10-24, 1973.

  • J. H. Ward, “Hierarchical grouping to optimize an objective function,” Journal of the American Statistical Association, vol. 58, no. 301, pp. 236-244, Mar. 1963.

  • Z. Li, H. Zhang, L. O’Brien, S. Jiang, Y. Zhou, M. Kihl, and R. Ranjan, “Spot pricing in the cloud ecosystem: A comparative investigation,” Journal of Systems and Software, vol. 114, pp. 1-19, Apr. 2016.


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  • Amazon EC2 Locations for Spot Pricing - A Hierarchical Clustering Approach

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Authors

Veena Khandelwal
Department of Computer Science & Engineering, SRM Institute of Science and Technology, India
Shantanu Khandelwal
Manager, KPMG Services Pte. Ltd.,, Singapore

Abstract


EC2 provides cloud services through several regions and availability zones. Spot instances are offered by Amazon EC2 through spot pricing. Amazon launched it simplified spot pricing model at Reinvent: 2017. The prices change less frequently now and are more predictable. To ensure availability, spot instances can be acquired from diversified locations. The biggest concern to the user is the identification of regions and availability zones to choose for optimizing cost and maximizing availability. The study attempts to find similarity in cost per instance among different EC2 locations according to Ward’s Hierarchical Clustering Algorithm. The analysis uses past 60 days history traces from last week of September to November 2019 of four different compute instance types across all Amazon EC2 regions and availability zones. Results suggest a significant amount of similarity in terms of cost of instance in spot pricing across different locations. This raises user’s confidence in adopting spot market.

Keywords


Amazon EC2 regions, Availability zones, Hierarchical clustering, Spot instances.

References