Publication Date

Fall 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Teng Moh

Second Advisor

Ramin Moazeni

Third Advisor

Melody Moh

Keywords

Content-based routing, Google Kubernetes Engine(GKE), resource optimization, load balancing, performance optimization, cluster-level load balancing

Abstract

Adaptive Metric-Driven Load Balancer is an innovative two-tier load-balancing system that uses NGINX and Prometheus to optimize resource allocation in specialized cloud clusters. This framework is built to give great performance and flexibility and runs on Google Kubernetes Engine (GKE), but it may also be deployed on local cloud environments for added security. The first tier of our system uses an NGINX-based load balancer to route incoming requests based on content type, sending traffic to hardware-optimized clusters for processing requests through specialized hardware. In our algorithm, the second tier dynamically modifies load distribution throughout each cluster by calculating pod weights based on CPU, memory, and network consumption on a regular basis. This adaptive weight distribution maximizes resource consumption and responsiveness, outperforming existing fixed-weight systems. In comparison to typical multi-component solutions, our system’s lightweight architecture minimizes configuration and management overhead while scaling fluidly to address dynamic traffic patterns. Its modular design paves a path for seamless integration with the existing cloud architectures providing organizations with a straightforward yet powerful solution for handling variable and unpredictable workloads. Our system provides high availability, optimal resource efficiency, and considerable performance increases under changing workloads due to its simplicity, scalability, and greater adaptability.

Available for download on Saturday, January 24, 2026

Share

COinS