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All Journal JOIV : International Journal on Informatics Visualization
Nehéz, Károly
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Computer Science & IT

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Efficient Broker-Driven Request Packet Size Sekhi, Ihab; Nehéz, Károly
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3131

Abstract

Efficient virtual machine (VM) allocation is fundamental in cloud computing to optimize resource utilization and ensure high performance. Traditional methods often fail to account for the variability in request packet sizes, resulting in inefficiencies and performance bottlenecks. This study introduces a novel broker-driven VM allocation approach integrated with fuzzy logic to optimize resource distribution and address these limitations dynamically. The proposed methodology employs a broker system for real-time monitoring and analysis of request packet sizes, leveraging fuzzy logic to adjust VM allocations based on fluctuating workload demands dynamically. Validation of the approach was conducted using real-world data from the Google Cloud Platform's Europe West3 region and t2d-standard machine types. Simulations executed with the Cloud Analyst tool across five scenarios demonstrated the method's efficacy compared to traditional techniques. The results from the third scenario were used as a representative example. Its findings include a 67.62% reduction in response time, a 26.64% decrease in data center processing time, a 26.65% improvement in request serving time, and a 70.65% reduction in total data transfer costs. The results of the other scenarios demonstrated comparable levels of improvement. The study highlights the effectiveness of a broker-driven, fuzzy logic-enhanced system in modern cloud computing, highlighting its adaptability and scalability. Future research should include incorporating energy consumption and fault tolerance parameters, applying the method to hybrid and multi-cloud environments, and integrating machine learning techniques.
Co-Authors Sekhi, Ihab