Automated Infrastructure Management and Resource Optimization in Cloud Environments

Authors

  • Sagar Kesarpu

Keywords:

Automated infrastructure management, resource optimization, cloud automation, Infrastructure as Code, AI-driven optimization, AIOps, self-healing infrastructure, cloud observability.

Abstract

Cloud computing has revolutionized enterprise infrastructures through scalability, flexibility, and provision of resources on demand. However, manually managing cloud infrastructures has become difficult due to the dynamism of the workloads, deployment in multiple clouds and changing resource demands. Infrastructure automation involves intelligent automation that is used to monitor, allocate, configure and optimise cloud infrastructure in real-time. Resource optimisation involves automation combined with artificial intelligence, machine learning and Infrastructure as Code to optimise infrastructure and improve its reliability. The system constantly monitors and analyses the usage patterns and behaviour of the workload along with the performance metrics in order to predict the capacity needs and accordingly optimise the computing, storage, and networking resources. Moreover, automation helps in making cloud computing operations better through smart allocation, configuration management, and self-healing. The automation process is capable of detecting any anomalies within the system and fixing the problems accordingly. This results in increased service availability, reduced downtime, and overall enhanced resiliency of the cloud environment in hybrid and multi-cloud settings. Continuous monitoring and observability also ensure real-time tracking of infrastructure performance, thus allowing for proactive improvement of its efficiency and fast troubleshooting. Integration of automation with DevOps and AIOps allows organisations to automate deployment processes, perform predictive maintenance of resources and implement governance policies for the resources. Therefore, consistency, improved scalability, and effective utilisation of the infrastructure are ensured.

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References

Baral, B., Ghimire, B., & Basnet, D. R. (2022). Understanding policy coherence and interplay governing biodiversity conservation and associated livelihood practices in Karnali province, Nepal. Nepal Public Policy Review, 27-91. https://nepjol.info/index.php/nppr/article/view/48395

Berglund, E. Z., Monroe, J. G., Ahmed, I., Noghabaei, M., Do, J., Pesantez, J. E., ... & Levis, J. (2020). Smart infrastructure: a vision for the role of the civil engineering profession in smart cities. Journal of Infrastructure Systems, 26(2), 03120001. https://ascelibrary.org/doi/abs/10.1061/(ASCE)IS.1943-555X.0000549

Holowaychuk, T. J. (2022). Adaptive Cloud Enterprise Framework for Secure Financial Systems AI Governance and Intelligent Workload Automation. International Journal of Emerging Trends in Engineering and Management Research, 7(5), 12537. http://ijetemr.com/index.php/ijetemr/article/view/62

Infosys. (2021). Infosys Annual Report 2020–21: Cloud chaos to clarity. Infosys Limited. Infosys Annual Report 2020-21. https://www.infosys.com/investors/reports-filings/annual-report/annual/documents/infosys-ar-21.pdf

Lakeside Team. (2019, June 18). Guide to AIOps tools in 2019: How to choose & key concepts. https://www.lakesidesoftware.com/blog/guide-aiops-tools-2019-how-choose-key-concepts/

Lins, S., Pandl, K. D., Teigeler, H., Thiebes, S., Bayer, C., & Sunyaev, A. (2021). Artificial intelligence as a service: classification and research directions. Business & Information Systems Engineering, 63(4), 441-456. https://link.springer.com/article/10.1007/s12599-021-00708-w

Liu, T., Huang, D., Tan, X., & Kong, F. (2020). Planning consistency and implementation in urbanizing China: Comparing urban and land use plans in suburban Beijing. Land Use Policy, 94, 104498. https://www.sciencedirect.com/science/article/pii/S026483771931525X

McKinsey, 2021. The state of AI in 2021. https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/Global%20survey%20The%20state%20of%20AI%20in%202021/Global-survey-The-state-of-AI-in-2021.pdf

McKinsey, 2022. The state of AI in 2022—and a half decade in review. McKinsey & Company. The state of AI in 2022. https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai%20in%202022%20and%20a%20half%20decade%20in%20review/the-state-of-ai-in-2022-and-a-half-decade-in-review.pdf

Mohammed, S. (2022). AI-driven IT operations and AIOps enablement across hybrid cloud platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4635-4639. https://www.ijeetr.com/index.php/ijeetr/article/view/1013

Murphy, O. (2022). Adoption of Infrastructure as Code (IaC) in Real World; lessons and practices from industry. https://www.theseus.fi/handle/10024/786729

Nagarajan, G. (2022). Optimizing project resource allocation through a caching-enhanced cloud AI decision support system. International Journal of Computer Technology and Electronics Communication, 5(2), 4812-4820. https://ijctece.com/index.php/IJCTEC/article/view/306

Patchamatla, P. S. S. (2022). A hybrid Infrastructure-as-Code strategy for scalable and automated AI/ML deployment in telecom clouds. International Journal of Computer Technology and Electronics Communication, 5(6), 6075-6083. https://ijctece.com/index.php/IJCTEC/article/view/240

Prakash, A. V., & Das, S. (2020). Intelligent conversational agents in mental healthcare services: a thematic analysis of user perceptions. Pacific Asia Journal of the Association for Information Systems, 12(2), 1. https://aisel.aisnet.org/pajais/vol12/iss2/1/

Sarilo-Kankaanranta, H., & Frank, L. (2021, October). The slow adoption rate of software robotics in accounting and payroll services and the role of resistance to change in innovation-decision process. In Conference of the Italian Chapter of AIS (pp. 201-216). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-031-10902-7_14

Soundappan, S. J. (2022). Modernizing Enterprise Software Ecosystems through Artificial Intelligence Driven Integration and Cloud Transformation. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9253. https://iadier-academy.org/index.php/IJFIST/article/view/477

Suryadevara, S. S. K. (2021). AI-Driven Multi-Cloud Orchestration System for Enterprise Digital Experience Delivery. American International Journal of Computer Science and Technology, 3(1), 21-34. http://aijcst.org/index.php/aijcst/article/view/205

Teru, K., Denis, E., & Hamilton, W. (2020, November). Inductive relation prediction by subgraph reasoning. In International conference on machine learning (pp. 9448-9457). PMLR. https://proceedings.mlr.press/v119/teru20a.html

Tomaszewski, L. E., Zarestky, J., & Gonzalez, E. (2020). Planning qualitative research: Design and decision making for new researchers. International journal of qualitative methods, 19, 1609406920967174. https://journals.sagepub.com/doi/abs/10.1177/1609406920967174

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Published

31.03.2022

How to Cite

Sagar Kesarpu. (2022). Automated Infrastructure Management and Resource Optimization in Cloud Environments. International Journal of Intelligent Systems and Applications in Engineering, 10(1s), 512–518. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8554

Issue

Section

Research Article