Araştırma Makalesi
BibTex RIS Kaynak Göster

An Empirical Analysis of Server Utilization and Optimization Strategies in a Proxmox VE Environment

Yıl 2025, Cilt: 2 Sayı: 2, 23 - 30, 15.12.2025

Öz

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.

Kaynakça

  • 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.

Yıl 2025, Cilt: 2 Sayı: 2, 23 - 30, 15.12.2025

Öz

Kaynakça

  • 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.
Toplam 10 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yönetim Bilişim Sistemleri
Bölüm Araştırma Makalesi
Yazarlar

Levent Emmungil 0000-0003-2220-5333

Gönderilme Tarihi 18 Kasım 2025
Kabul Tarihi 26 Kasım 2025
Yayımlanma Tarihi 15 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 2 Sayı: 2

Kaynak Göster

APA Emmungil, L. (2025). An Empirical Analysis of Server Utilization and Optimization Strategies in a Proxmox VE Environment. Uygulamalı Mühendislik ve Tarım Dergisi, 2(2), 23-30.