Reducing Latency Through Adaptive Load Balancing in Multi-Cloud Kubernetes

Basavaraja, Yashvanth Shivamurthy (2025) Reducing Latency Through Adaptive Load Balancing in Multi-Cloud Kubernetes. Masters thesis, Dublin, National College of Ireland.

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Abstract

The rapid uptake of cloud computing and increasing internet traffic have made effective load balancing in multi-cloud Kubernetes setups a pressing requirement. Traditional methods are deficient in meeting the complexities of multi-cloud setups, leading to excessive latency and resource misuse. This contribution presents a new framework that combines Content Delivery Networks (CDNs) and adaptive load balancing between AWS and Azure Kubernetes Service (AKS) clusters. With the help of advanced techniques like multi-level caching, latency-sensitive routing, and heuristic algorithms like Consistent Hashing with Bounded Loads and Least Latency with Capacity-Aware Weighting, the solution aims to reduce latency and resource usage in an optimized way. HTTP/3 integration and edge zones also decrease connection delay and minimize distance to move content closer to users. The study targets latencies below 150ms and CPU balancing, addressing the problems of inter-cloud latency and resource variability. This paper presents a cost-effective, scalable, and high-performance solution to modern cloud applications that opens the door to the future development of cloud computing by proposing a framework for optimized resource utilization and latency reduction.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pulido Gaytan, Luis Bernardo UNSPECIFIED
Subjects:	T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Electronic computers. Computer science > Computer Systems > Computers > Electronic data processing > Electronic data processing--Distributed processing > Edge computing T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science > Computer Systems > Computers > Electronic data processing > Electronic data processing--Distributed processing > Edge computing
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	20 Mar 2026 11:18
Last Modified:	20 Mar 2026 11:18
URI:	https://norma.ncirl.ie/id/eprint/9199

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