AWS Spot Instances: A Cloud Computing Cost Investigation Across AWS Regions

Authors

  • Parul Dubey Dr C V Raman University, Bilaspur, Chattishgarh, India
  • Arvind Kumar Tiwari Dr C V Raman University, Bilaspur, Chattishgarh, India

Keywords:

Amazon Web Services, Availability Zone (AZ), Elastic Compute Cloud, Regions, Spot Instances, Virtual Machines

Abstract

Renting virtual machines (VMs) on AWS offers cost-effective and scalable computing resources with global reach, allowing businesses to easily scale up or down based on their needs. With a wide range of VM types, robust security features, high reliability, and easy management tools, businesses can deploy VMs in multiple regions worldwide, ensuring optimal performance, security, and operational efficiency. Making a choice about these VM’s is an important task. This research paper analysed the costs of 17 different instances across 17 different regions in the AWS cloud platform. The analysis considered instances from three different groups, and in all three groups, us-east-2 had lower costs compared to other regions. Specifically, in the general-purpose instances category, us-east-2a consistently offered lower prices than other regions throughout the considered time period, with some fluctuations for certain instances. The m7g.2xlarge instance, available in limited regions, was found to be offered at a minimum cost in us-east-2. Additionally, randomly selected instances from different categories, also showed that us-east-2 had lower costs compared to other regions for most instances. Although t2.large instances were not available in us-east-2a, they were found to be cheaper in us-west-2. This research suggests that organizations seeking cost-effective cloud computing solutions may benefit from selecting us-east-2 as their preferred AWS cloud platform region.

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References

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, Published, doi: 10.1109/ccgrid.2018.00059.

L. Lin, L. Pan, and S. Liu, “Methods for improving the availability of spot instances: A survey,” Computers in Industry, vol. 141, p. 103718, Oct. 2022, doi: 10.1016/j.compind.2022.103718.

J. Fabra, J. Ezpeleta, and P. Álvarez, “Reducing the price of resource provisioning using EC2 spot instances with prediction models,” Future Generation Computer Systems, vol. 96, pp. 348–367, Jul. 2019, doi: 10.1016/j.future.2019.01.025.

S. S. Nezamdoust, M. A. Pourmina, and F. Razzazi, “Optimal prediction of cloud spot instance price utilizing deep learning,” The Journal of Supercomputing, vol. 79, no. 7, pp. 7626–7647, Dec. 2022, doi: 10.1007/s11227-022-04970-x.

A. Baldominos Gómez, Y. Saez, D. Quintana, and P. Isasi, “AWS PredSpot: Machine Learning for Predicting the Price of Spot Instances in AWS Cloud,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 7, no. 3, p. 65, 2022, doi: 10.9781/ijimai.2022.02.003.

“Amazon EC2 - Secure and resizable compute capacity – Amazon Web Services,” Amazon Web Services, Inc. https://aws.amazon.com/ec2/pricing/

“Global Infrastructure,” Amazon Web Services, Inc. https://aws.amazon.com/about-aws/global-infrastructure/

“Best practices for EC2 Spot - Amazon Elastic Compute Cloud,” Best practices for EC2 Spot - Amazon Elastic Compute Cloud. https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-best-practices.html

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Published

25.12.2023

How to Cite

Dubey, P. ., & Tiwari, A. K. . (2023). AWS Spot Instances: A Cloud Computing Cost Investigation Across AWS Regions . International Journal of Intelligent Systems and Applications in Engineering, 12(1), 01–10. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3671

Issue

Vol. 12 No. 1 (2024)

Section

Research Article

License

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