Efficient Resource Utilization with Auto Tagging Using Amazon`s Cloud Trail Services - Makani, Sai Teja (International Journal of Computer Sciences and Engineering, 2023)
In the realm of resource management, the practice of labeling, ledgering, and tagging has a rich historical legacy that has transcended time, demonstrating its enduring importance. These processes, which have been fundamental since ancient times, continue to wield immense significance in the contemporary context, particularly when applied to intangible assets, which are instrumental in organizational success. However, in the contemporary landscape characterized by digitalization and the proliferation of non-tangible assets, the task of effectively labeling and tagging resources has grown markedly intricate. This complexity is especially conspicuous when considering intangible resources, and it is accentuated within the domain of software infrastructure and application modules. In these domains, the sheer volume of resources in use has burgeoned to unprecedented levels, rendering manual tagging processes not only labor-intensive but also prone to errors and inconsistencies. Moreover, the scope of resource tagging has evolved beyond the rudimentary labeling of resource names, now encompassing a multitude of metadata attributes that impart comprehensive context and information. To tackle these formidable challenges, this paper presents a robust, enterprise-grade solution engineered to automate the resource tagging processes within the Amazon Web Services (AWS) ecosystem. At its core, this solution leverages the capabilities offered by Amazon's CloudTrail services, harnessing them to mitigate the manual burden associated with resource tagging activities...... .