Optimizing Cloud Deployment: Blending of IaaS and FaaS for Microservice Architecture

2026-06-10

AI Summary

This work addresses the challenges of resource utilization and operational efficiency in microservice architectures by proposing a performance-metric-driven automated framework that intelligently determines the optimal deployment strategy for individual microservices between Infrastructure-as-a-Service (IaaS) and Function-as-a-Service (FaaS). By analyzing intrinsic microservice characteristics, the framework enables a scalable and reproducible migration from conventional IaaS deployments to a hybrid IaaS+FaaS model. Experimental evaluation on two real-world applications demonstrates that the approach accurately identifies microservices well-suited for serverless execution, significantly improving both deployment efficiency and resource utilization. Furthermore, the study clarifies the respective applicability boundaries and advantages of different cloud service models, offering practical guidance for architecture design in heterogeneous cloud environments.

Abstract

The rapid evolution of cloud computing has resulted in the adoption of hybrid deployments that blend Infrastructure-as-a-Service (IaaS) and Function-as-a-Service (FaaS) service models to optimize resource utilization, scalability, and operational efficiency. This paper presents a comprehensive study and practical implementation of a metrics-driven approach for migrating microservices from a traditional IaaS service model to a hybrid IaaS + FaaS model, using two microservice applications as case studies. The research develops an automated framework to analyze service-level performance metrics to identify microservices that are best suited for serverless execution. The findings of our research highlight the benefits and limitations of different cloud service models and provide a scalable and replicable automated methodology for optimized deployment of cloud-native applications.

Problem

• Cloud Deployment
• IaaS
• FaaS
• Microservice Architecture
• Serverless

Innovation

• hybrid cloud deployment
• metrics-driven migration
• serverless computing
• microservice optimization
• automated framework