INTEGRATION OF PROCESS OPTIMIZATION METHODS FOR LATENCY REDUCTION IN DISTRIBUTED CLOUD SYSTEMS

DOI: 10.31673/2786-8362.2024.028517

Abstract

This article explores the integration of process
optimization methods to address latency challenges in distributed cloud systems. The research focuses on
enhancing cloud infrastructure performance by developing adaptive algorithms for resource management and
dynamic routing. The proposed methods incorporate real-time decision-making capabilities to allocate
resources and manage traffic efficiently, resulting in a significant latency reduction of 30-40% compared to
conventional approaches.
The study outlines the design and implementation of optimization models and provides a detailed analysis
of their effectiveness through simulation experiments. The results demonstrate that the methods reduce delays
and improve resource utilization and overall system throughput. Practical applications include optimized cloud
operations for service providers and enhanced user satisfaction due to improved service quality.
Additionally, the research highlights the potential for future advancements by further integrating machine
learning and artificial intelligence to refine predictive capabilities and automation in resource management.
These developments open new avenues for innovation in cloud system administration and monitoring.

Keywords: cloud systems, process optimization, latency reduction, adaptive algorithms, resource
management, dynamic routing, distributed computing, machine learning

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