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Optimizing Cloud Performance: Best Practices for Testing in Scalable Digital Environments

Author(s) Santosh Kumar Jawalkar
Country United States
Abstract The deployment and management of digital services depends heavily on cloud computing through its ability to provide scalability along with management flexibility and reduced costs. Optimizing cloud performance involves several obstacles such as resource contention problems autoscaling system inefficiencies as well as data migration complications. Ensuring reliability and efficiency in cloud environments requires robust testing strategies that address performance bottlenecks, failover mechanisms, and data integrity. This research aims to identify best practices for optimizing cloud performance through effective testing methodologies, focusing on load testing, autoscaling validation, and data migration testing to improve system resilience and user satisfaction.
The research utilized a secondary research approach through analysis of existing work from the established literature sources IEEE the ACM and Springer. A pre-defined methodology directed the study through stages including literature gathering, data gathering from sources developing themes which were then integrated into final conclusions. The research categorized key focus areas such as load and performance testing, autoscaling and failover validation, and data migration testing. Various tools and strategies were evaluated to identify their effectiveness, strengths, and challenges, providing a comparative analysis of cloud performance optimization techniques.
The findings revealed that real-time monitoring and automated testing frameworks significantly enhance cloud application reliability and scalability. Predictive autoscaling mechanisms driven by AI can optimize resource allocation, while mock migration and schema validation help ensure seamless data transfers with minimal downtime. Researchers detected shortcomings in current methods when dealing with hybrid cloud models as well as cost-performancing balances.
In conclusion, organizations should adopt automated performance monitoring, predictive autoscaling, and comprehensive data validation techniques to optimize cloud performance. Regular testing, proactive fault detection, and industry-aligned strategies are essential to maintaining cloud efficiency and resilience. Future research should focus on integrating AI-driven solutions to further enhance cloud performance testing methodologies.
Keywords Cloud Performance Optimization, Load Testing, Performance Testing, Autoscaling Validation, Failover Mechanisms, Data Migration Testing, Cloud Scalability, Resource Allocation, Predictive Autoscaling, Real-time Monitoring, Cloud Reliability, Cloud Efficiency, Schema Validation, Mock Migration, Cloud Cost Optimization, Chaos Engineering, Cloud Testing Tools, Cloud Resource Management, Cloud Transition Strategies, Hybrid Cloud Challenges, Data Integrity, Automated Cloud Testing, Cloud Resilience, Service-Level Agreements (SLAs), Cloud Computing Best Practices, AI-driven Cloud Optimization.
Field Engineering
Published In Volume 11, Issue 2, April-June 2020
Published On 2020-04-09
Cite This Optimizing Cloud Performance: Best Practices for Testing in Scalable Digital Environments - Santosh Kumar Jawalkar - IJSAT Volume 11, Issue 2, April-June 2020. DOI 10.5281/zenodo.14802784
DOI https://doi.org/10.5281/zenodo.14802784
Short DOI https://doi.org/g83xks
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