Cost Optimization - A Recommendation Analysis of Azure Workloads
Keywords:data analysis, cloud computing, cost optimization, recommendation algorithm, microsoft azure
In a rapidly modernizing world, cloud computing has emerged as an innovative and ground-breaking technology. It is a sector that is experiencing rapid growth, accelerated by the COVID-19 pandemic and remote work environment. Many businesses have started to make the transition from traditional storage systems to the cloud. However, the transition can be opaque and result in an excessive amount of spending on cloud resources. Additionally, these resources are often underutilized, further contributing to cloud waste. This paper seeks to address this problem by designing a comprehensive algorithm based on a detailed analysis of the Microsoft Azure 2019 virtual machine (VM) traces. We first analyzed the cost of approximately 2.7 million VMs and identified that many VMs are under utilized and over consume resources. Then, using a policy-oriented algorithm we constructed a list of VMs that were considered to be "wasteful". Finally, we implemented a recommendation algorithm to sort through this constructed list and recommend lower prices and options for users without interfering with the purpose and function of the VM. Our results show that if all users were to follow our recommendations, the potential cost reduction is over \$1.4 million. We conclude with a discussion of the implications of this research and make recommendations for future studies.
References or Bibliography
Aljabre, A. (2012). Cloud Computing for Increased Business Value. International Journal of Business and Social Science, 3(1), 234-239.
Buntain, C.L., Bonneau, R., Nagler, J., & Tucker, J.A. (2021). YouTube Recommendations and Effects on Sharing Across Online Social Platforms. Proceedings of the ACM on Human-Computer Interaction, 5, 1 - 26. https://doi.org/10.1145/3449085.
Everman, B., Gao, M., Zong, Z. (2022). Evaluating and reducing cloud waste and cost—A data-driven case study from Azure workloads. Sustainable Computing: Informatics and Systems, 35. https://doi.org/10.1016/j.suscom.2022.100708.
Flexera. (2022). 2022 State of the Cloud Report.
Mehlhose, F., Petrifke, M., Lindemann, C. (2021). Evaluation of Graph-based Algorithms for Guessing User Recommendations of the Social Network Instagram. 2021 IEEE 15th International Conference on Semantic Computing, 409-414. https://doi.org/10.1109/ICSC50631.2021.00075.
Namasudra, S. (2018). Cloud computing: A new era. Journal of Fundamental and Applied Sciences, 10(2), 113-135.
Shahrad, M., Fonesca, R., Goiri, I., Chaudhry, G., Batum, P., Cooke, J., Laureano, E., Tresness, C., Russinovich, M., Bianchini, R. (2020). Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider. 2020 USENIX Annual Technical Conference, 205-218.
Truong, D. (2010). How Cloud Computing Enhances Competitive Advantages: A Research Model for Small Businesses. The Business Review, 15(1), 59-65.
Vilarinho, E.C., Bonde, A., Muzio, A., Russinovich, M., Fontoura, M., & Bianchini, R. (2017). Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. Proceedings of the 26th Symposium on Operating Systems Principles, 153-167. https://doi.org/10.1145/3132747.3132772.
Zhang, M., Liu, Y. (2021). A commentary of TikTok recommendation algorithms in MIT Technology Review 2021. Fundamental Research, 1(6), 846-847. https://doi.org/10.1016/j.fmre.2021.11.015.
How to Cite
Copyright (c) 2022 Yuxuan Zhang, David Li, Sophia Zhang
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.