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Mastering Cloud-Native Performance: Strategies for Optimization

Author(s) Siva Kumar Mamillapalli, Ramya Devi Jeganathan
Country United States
Abstract This research addresses the critical issue of suboptimal resource utilization and response times in dynamic cloud environments by identifying and evaluating effective performance tuning techniques for cloud-native applications. Through a comprehensive collection and thorough analysis of performance metrics, user experience data, and resource consumption statistics sourced from a variety of cloud-native applications, the study highlights several key techniques that result in significant enhancements in application performance, efficiency, and overall user satisfaction. Notably, the findings demonstrate that the implementation of adaptive scaling and optimization algorithms can lead to a reduction in latency by up to 30% and improve resource utilization by over 25%, thereby enhancing the overall system responsiveness and efficiency. By disseminating effective performance tuning strategies, this research contributes meaningfully to the ongoing evolution of cloud computing practices, fostering not only improved operational efficiencies and enhanced service delivery but also supporting innovation across diverse sectors. As cloud-native applications continue to play a crucial role in numerous fields, the insights gained from this study are poised to help organizations navigate the complexities of cloud environments and drive forward their digital transformation initiatives.
Keywords Cloud Native Applications, Performance Tuning, Resource Optimization, Observability, Latency Reduction
Field Engineering
Published In Volume 3, Issue 3, March 2022
Published On 2022-03-09
Cite This Mastering Cloud-Native Performance: Strategies for Optimization - Siva Kumar Mamillapalli, Ramya Devi Jeganathan - IJLRP Volume 3, Issue 3, March 2022. DOI 10.5281/zenodo.14960456
DOI https://doi.org/10.5281/zenodo.14960456
Short DOI https://doi.org/g86v46
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