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All Journal IJIES (International Journal of Innovation in Enterprise System)
M. Khaerul Ihsan
Universitas Bumigora
Control & Systems Engineering Engineering

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Implementation of Auto-Scaling and Load Balancing on Proxmox VE Using Python and REST API Naufal Hanif; Dading Oktaviadi Resmiranta; M. Khaerul Ihsan; Tanwir Tanwir; I Putu Hariyadi; Ondi Asroni
IJIES (International Journal of Innovation in Enterprise System) Vol 10 No 2 (2026): International Journal of Innovation in Enterprise System - Article in Press
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijies.v10i02.10429

Abstract

Auto-scaling and load balancing are essential for maintaining web service availability and performance under fluctuating workloads. This research implements an automated auto-scaling and load balancing system on Proxmox Virtual Environment (Proxmox VE) leveraging Proxmox REST API through Python scripts. The system monitors web container CPU usage at 5-second intervals, triggering scale-out by cloning LXC containers when average CPU exceeds 75% and scale-in when it falls below 30%, while ensuring at least one container remains active. Testing on a laboratory topology consisting of 1 template container, 1 load balancer, and 4 backend containers (IP range: 192.168.100.251-192.168.100.254) demonstrates average provisioning time of 96.0 seconds (range: 85.5-117.7s) with breakdown: API Clone (82.4s), Network Config (0.0s), Container Start (6.8s), Content Customization (3.1s), and Load Balancer Update (3.6s). Comparative analysis between always-on (4 containers) and auto-scaling scenarios reveals 36.1% CPU savings (31.66 vs 49.57 CPU-hours), idle time reduction from 54.2% to 28.3%, and efficient resource utilization with the system operating predominantly on 1 container (85% uptime). This implementation proves that Proxmox API integration with Python-based automation provides a practical auto-scaling solution for private clouds without complex orchestration platforms like Kubernetes.
Co-Authors Dading Oktaviadi Resmiranta I Putu Hariyadi Naufal Hanif Ondi Asroni Tanwir Tanwir