Diagnosing and Resolving High CPU Utilization in Cloud Databases
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DOI:
https://doi.org/10.67228/30715636/WCMEAI-2025P108Published 03-22-2025
Kernel Profiling, Cloud Infrastructure, AI-Powered Diagnosis, Performance Bottlenecks, Kernel Instrumentation, Anomaly Detection, Distributed Systems, Observability, KernelSight-AI Issue
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ArticlesHow to Cite
[1]S. S. Allenki, “Diagnosing and Resolving High CPU Utilization in Cloud Databases”, IJETMR, pp. 119–137, Mar. 2025, doi: 10.67228/30715636/WCMEAI-2025P108.Abstract
High CPU use in cloud-based databases is a usual performance problem that affects how rapidly applications respond, how well they can grow & how well the entire architecture works. This study looks at the main causes, distinguishing tools & perfect approaches for lowering extravagant CPU utilization in virtualized database inauguration. The study depicts various contributing factors via observational analysis & controlled reproduction, encompassing inefficient query implementation plans, absent or outdated indexing, heightened transaction concurrency, improperly adjusted their resource allocation & suboptimal framework of caching or connection pooling mechanisms. The diagnostic process uses tools for monitoring their performance, profiling queries & analyzing workload patterns to find performance hotspots with great accuracy. Continuous monitoring application indications like CPU load averages, query latency & input/output production is more important for finding many problems early on. The suggested system significantly strengthens these resource efficiency via the use of adaptive workload management, query optimization & the automated scaling approaches, all while preserving data integrity & throughput. The experimental findings from the case study indicate that intelligent query optimization & load balancing may reduce CPU use by over 40%. Dynamic scaling and caching its improvements, on the other hand, make performance more steady when there is a lot of demand. The research emphasizes the need of conveying their database setups with workload factors instead of depending on their static stipulations The findings show that cautious diagnosis & systematized their enhancement of cloud databases may greatly increase their performance consistency, cost-effectiveness & the scalability. By keeping an eye on their database systems all the time, optimizing them based on their information, and automating processes in the best way, businesses can keep them functioning very smoothly. This manner, the systems can simply deal with many changes in workload. This research offers significant insights for database administrators, cloud architects & DevOps teams focused on creating resilient, resource-efficient & more scalable cloud infrastructures.
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How to Cite
[1]S. S. Allenki, “Diagnosing and Resolving High CPU Utilization in Cloud Databases”, IJETMR, pp. 119–137, Mar. 2025, doi: 10.67228/30715636/WCMEAI-2025P108.