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Enterprise cloud resource optimization and management based on cloud operations
- 1 Heating Ventilation and Air Conditioning Engineering, Tsinghua University, Beijing China
- 2 Computer & Information Technology, Northern Arizona University, Flagstaff, AZ, USA
- 3 Electrical & Computer Engineering, New York University, New York, NY, USA
- 4 Executive Master of Business Administration, Amazon Connect Technology Services (Beijing) Co., Ltd. Xi’an, Shaanxi, China
- 5 Information Studies, Trine University, Phoenix USA
- 6 Computer Information Technology, Northern Arizona University, Flagstaff, AZ, USA
* Author to whom correspondence should be addressed.
Abstract
The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.
Keywords
Digitization, IT Operations (ITOps), Cloud Computing, Resource Management, Automation
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Cite this article
Wu,B.;Gong,Y.;Zheng,H.;Zhang,Y.;Huang,J.;Xu,J. (2024). Enterprise cloud resource optimization and management based on cloud operations. Applied and Computational Engineering,76,8-14.
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Volume title: Proceedings of the 2nd International Conference on Software Engineering and Machine Learning
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