Methods for Minimizing Request Processing Latency in Microservices Architecture

Artem Iurchenko

Citation: Artem Iurchenko, "Methods for Minimizing Request Processing Latency in Microservices Architecture", Universal Library of Innovative Research and Studies, Volume 02, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article examines the issue of reducing request processing latency in a microservices architecture deployed in a cloud environment. Key factors contributing to increased latency are identified, including frequent inter-service communication, uneven distribution of computational resources, and the lack of a comprehensive fault-tolerance strategy. A scheduling strategy based on a modified particle swarm optimization (PSO) algorithm and an extended Round Robin (RR) algorithm is proposed. The modified PSO accounts for the microservices call graph and the physical proximity of nodes within predefined threshold constraints, while the RR algorithm ensures balanced load distribution and eliminates single points of failure. The effectiveness of the approach was evaluated using datasets (traces) from Alibaba and Google, reflecting real-world microservices operation scenarios. Experimental results demonstrated a reduction in network traffic (up to 35%), a decrease in latency (by 80% or more in static scenarios), and a more uniform resource utilization (reducing standard deviation by 40–50%). To apply the described methodology (PSO+RR), it should be integrated with an orchestration system (Kubernetes), achieving a systematic improvement in both performance and fault tolerance. The findings presented in this article are intended for system architects, developers, and DevOps engineers seeking to optimize microservices system performance by minimizing request processing latency. The results can be incorporated into existing DevOps practices and applied in large data centers.


Keywords: Microservices, Cloud Computing, Latency, Particle Swarm Optimization, Scheduling, Load Balancing, Fault Tolerance, Kubernetes.

Download doi https://doi.org/10.70315/uloap.ulirs.2025.0202003