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 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Algorithmic model for cloud performance optimization using connection pooling technique

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pp. 489–499Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1290XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1290
Pages:
489–499

Abstract

This study introduces an algorithmic framework aimed at boosting the efficiency of cloud computing systems by employing connection pooling techniques. In the realm of cloud environments, optimizing performance is paramount to ensuring streamlined resource usage and meeting user requirements. Conventional methods of managing connections in cloud systems often lead to overhead and inefficiencies due to the constant creation and termination of connections. To tackle this issue, our proposed algorithmic framework utilizes connection pooling, a widely adopted technique in computer programming, to manage connections with greater efficiency. Through the consolidation and reuse of connections, our framework targets to diminish latency, enhance throughput, and improve overall system scalability. We validate and refine our algorithmic framework through simulations and experiments across diverse cloud computing scenarios, showcasing its efficacy in performance optimization while curbing resource consumption. Our findings underscore the potential of connection pooling techniques in alleviating performance bottlenecks and optimizing cloud infrastructure for varied workloads. This study contributes to the progression of cloud computing by presenting a pragmatic approach to enhancing system performance and scalability.

Keywords

Subject Classifications

49M3049M4165K10

References

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