An Empirical Analysis of Server Utilization and Optimization Strategies in a Proxmox VE Environment
Abstract
Server underutilization in enterprise and institutional data centers represents a significant source of financial waste and environmental impact. While proprietary virtualization platforms are extensively documented in literature, open-source alternatives like Proxmox Virtual Environment (VE) require further empirical analysis to validate their role in cost-effective IT management. This paper presents a detailed case study investigating the resource utilization of two heterogeneous Proxmox VE servers deployed in a corporate data center. Through a multi-resolution analysis (yearly, monthly, weekly, daily, hourly) of telemetry data – including CPU, memory, swap, load average, and network statistics – we diagnose critical inefficiencies: severe memory over-commitment on one server and systemic underutilization on the other. These patterns highlight common pitfalls such as imbalanced resource allocation, inadequate workload distribution, and insufficient VM right-sizing. Based on our findings, we propose a set of actionable optimization strategies, including cross-server workload migration, dynamic resource allocation, and energy-aware consolidation. Our results, derived from a real-world deployment in one of the biggest technopolis in Türkiye, demonstrate that a data-driven approach to managing Proxmox VE infrastructures can substantially reduce capital and operational expenditures, mitigating the rising costs of server hardware and operations.
Keywords
Server Virtualization, Resource Optimization, Proxmox VE, Data-Driven Management, Cost Efficiency
References
- Ajankar, S., Mohta, A., & Sane, S. (2011). Optimization in virtualization. https://doi.org/10.1007/978-81-8489-989-4_16
- Barroso, L. A., & Hölzle, U. (2009). The case for energy-proportional computing. IEEE Computer, 40(12), 33–37.
- Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768. https://doi.org/10.1016/j.future.2011.04.017
- Clark, C., Fraser, K., Hand, S., Hansen, J., Jul, E., Limpach, C., Pratt, I., & Warfield, A. (2005). Live migration of virtual machines. In Proceedings of the 2nd Symposium on Networked Systems Design & Implementation (NSDI).
- Cost optimization in dedicated servers in 2025. (2025). UMA Technology. https://umatechnology.org/cost-optimization-in-dedicated-servers-in-2025/ Toxigon Infinite. (2025). How to optimize server performance in 2024. https://toxigon.com/how-to-optimize-server-performance-in-2024
- Hsu, C. H., Chen, Y., Wang, T., & Wu, C. (2011). Energy-aware task consolidation technique for cloud computing. In 2011 IEEE 3rd International Conference on Cloud Computing Technology and Science (pp. 115–121). IEEE. https://doi.org/10.1109/CloudCom.2011.25
- Peng, J., Chen, J., Kong, S., Liu, D., & Qiu, M. (2016). Resource optimization strategy for CPU intensive applications in cloud computing environment. In 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) (pp. 124–128). IEEE. https://doi.org/10.1109/CSCloud.2016.29
- Proxmox Server Solutions GmbH. (2025). Proxmox VE documentation. https://pve.proxmox.com/pve-docs/
- Singh, H., Bhasin, A., Kaveri, P. R., & Chavan, V. (2020). Cloud resource management: Comparative analysis and rese-arch issues. International Journal of Scientific & Technology Research, 9(6), 96–113.
- Verma, A., Ahuja, P., & Neogi, A. (2008). pMapper: Power and migration cost aware application placement in virtualized systems. In Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware.