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Journals / Karaelmas Fen ve Mühendislik Dergisi / 2026 / Cilt: 16 Sayı: 1

Digital Twin-Enabled Resource Allocation and Performance Optimisation in Cloud-Based Complex Data Workflows: A Particle Swarm Optimisation Approach

Bulut Tabanlı Karmaşık Veri İş Akışlarında Dijital İkiz Destekli Kaynak Tahsisi ve Performans Optimizasyonu: Parçacık Sürüsü Optimizasyonu Yaklaşımı

Ümit Demirbaga(BARTIN ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ, BİLGİSAYAR MÜHENDİSLİĞİ PR.)0000-0001-5159-0723
Pages
75–89
Publication Date
2026-04-21
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Experimental results show that the proposed PSO solution achieves better makespan reduction and improved resource utilisation than the baseline greedy allocation method across various multi-VM configurations.

📌 3 Key Takeaways

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Objective & Hypothesis:In large-scale cloud-based big data systems, optimal resource allocation remains challenging due to the variety of workloads and limited computational resources.
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Key Findings & Data:Using digital twin technology, we demonstrate our methodology through an optimisation approach to resource allocation and performance tuning for a cloud-based, complex data processing application.
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Conclusion & Impact:Experimental results show that the proposed PSO solution achieves better makespan reduction and improved resource utilisation than the baseline greedy allocation method across various multi-VM configurations.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Bulut bilişim · dijital ikiz · parçacık sürüsü optimizasyonu

Abstract

In large-scale cloud-based big data systems, optimal resource allocation remains challenging due to the variety of workloads and limited computational resources. Using digital twin technology, we demonstrate our methodology through an optimisation approach to resource allocation and performance tuning for a cloud-based, complex data processing application. Contrary to the conventional heuristic or genetic algorithms popular in the literature, this paper applies Particle Swarm Optimisation (PSO) to efficiently solve the task-to-resource assignment problem. We obtain a synthetic yet realistic workload that includes task execution times, resource usage profiles, and data locality characteristics. The model above addresses the issue by minimising the makespan while imposing penalty constraints for infeasible scheduling and minimising CPU and memory utilisation. Experimental results show that the proposed PSO solution achieves better makespan reduction and improved resource utilisation than the baseline greedy allocation method across various multi-VM configurations. The proposed approach offers a novel and valuable optimisation tool that can be widely applied to cloud workload management by integrating digital twin frameworks.

Özet

Büyük ölçekli bulut tabanlı büyük veri problemlerinde, çeşitli iş yükleri ve sınırlı hesaplama kaynakları nedeniyle optimal kaynak tahsisi hâlâ önemli bir zorluktur. Bu çalışmada dijital ikiz teknolojisini kullanarak, kaynak tahsisi ve performans ayarlaması için bir bulut tabanlı karmaşık veri işleme uygulamasında optimizasyon temelli bir yöntem önerilmiştir. Literatürde yaygın olarak kullanılan geleneksel sezgisel veya genetik algoritmaların aksine, bu çalışmada görev-kaynağa atama problemini etkin bir şekilde çözmek amacıyla Parçacık Sürüsü Optimizasyonu (PSO) uygulanmıştır. Gerçekçi ve sentetik bir iş yükü veri seti oluşturularak görev yürütme süreleri, kaynak kullanım profilleri ve veri yerelliği özellikleri dikkate alınmıştır. Geliştirilen model, uygun olmayan planlamalar için ceza kısıtları altında toplam işlem süresini minimize etmeyi ve aynı zamanda CPU ve bellek kullanımını dengelemeyi hedeflemektedir. Deneysel sonuçlar, önerilen PSO çözümünün, çoklu VM konfigürasyonları altında, temel greedy tahsis yöntemine kıyasla daha iyi makespan azaltımı ve kaynak kullanım dengesi sağladığını göstermektedir. Önerilen yaklaşım, dijital ikiz çerçeveleri ile bütünleştirilerek bulut iş yükü yönetimi alanında geniş uygulama potansiyeline sahip yeni ve etkili bir optimizasyon aracı sunmaktadır.

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Cite This Article

Demirbaga, Ü., *, Ü. D. (2026). Digital Twin-Enabled Resource Allocation and Performance Optimisation in Cloud-Based Complex Data Workflows: A Particle Swarm Optimisation Approach. Karaelmas Fen ve Mühendislik Dergisi, Cilt: 16 Sayı: 1, 75–89. https://doi.org/10.7212/karaelmasfen.1794485

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References (23)

  1. Aggarwal, S., Kumar, N., Singh, A., & Aujla, G. S. (2024). Blocktwins: Blockchain empowered supply chain digital twins in metaverse. 2024 IEEE International Conference on Communications Workshops (ICC Workshops), 1456–1461.
  2. Alkayal, E. (2018). Optimizing resource allocation using multi-objective particle swarm optimization in cloud computing systems. Doctoral Thesis, University of Southampton, UK.
  3. Clerc, M., & Kennedy, J. (2002). The particle swarm – explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.
  4. Demchenko, Y., Turkmen, F., de Laat, C., Hsu, C. H., Blanchet, C., & Loomis, C. (2017). Cloud computing infrastructure for data intensive applications. In Big Data Analytics for Sensor-Network Collected Intelligence (pp. 21–62). Elsevier.
  5. Demirbaga, Ü., Aujla, G. S., Jindal, A., & Kalyon, O. (2024). Big data analytics. In Big Data Analytics: Theory, Techniques, Platforms, and Applications (pp. 31–42). Springer.
  6. Dua, A., Aujla, G. S., Jindal, A., & Sun, H. (2024). Green AutoML: Energy-efficient AI deployment across the edge–fog–cloud continuum. 2024 IEEE Globecom Workshops (GC Wkshps), 1–6.
  7. Ghafari, R., Kabutarkhani, F. H., & Mansouri, N. (2022). Task scheduling algorithms for energy optimization in cloud environment: A comprehensive review. Cluster Computing, 25(2), 1035–1093.
  8. Jomah, S., & Aji, S. (2024). Meta-heuristic scheduling: A review on swarm intelligence and hybrid meta-heuristics algorithms for cloud computing. Operations Research Forum, 5(4), 94.
  9. Kabir, M. R., Halder, D., & Ray, S. (2024). Digital twins for IoT-driven energy systems: A survey. IEEE Access, 12, 177123–177143.
  10. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
  11. Li, M., Gao, J., Zhou, C., Shen, X., & Zhuang, W. (2022). Digital twin-driven computing resource management for vehicular networks. GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 5735–5740.
  12. Lin, W., Wu, M., Deng, D., & Chang, W. (2019). An energy-efficient task scheduling algorithm based on PSO in cloud environments. Journal of Systems Architecture, 98, 14–25.
  13. Malti, A. N., Hakem, M., & Benmammar, B. (2024). A new hybrid multi-objective optimization algorithm for task scheduling in cloud systems. Cluster Computing, 27(3), 2525–2548.
  14. Ni, C., Wu, J., & Wang, H. (2025). Energy-aware edge computing optimization for real-time anomaly detection in IoT networks. Applied and Computational Engineering, 139, 42–53.
  15. Prasad, R., Roy, A., & Kumari, S. (2025). Enhancing cloud task scheduling using a hybrid particle swarm and grey wolf optimisation approach. International Journal of Cloud Applications and Computing (IJCAC), 15(1), 32–47. https://doi.org/10.4018/IJCAC.326106
  16. Qiu, S., Zhao, J., Lv, Y., Dai, J., Chen, F., Wang, Y., & Li, A. (2022). Digital-twin-assisted edge-computing resource allocation based on the whale optimization algorithm. Sensors, 22(23), 9546. https://doi.org/10.3390/s22239546
  17. Saxena, D., & Singh, A. K. (2025). A self-healing and fault-tolerant cloud-based digital twin processing management model. IEEE Transactions on Industrial Informatics.
  18. Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. 1998 IEEE International Conference on Evolutionary Computation, 69–73.
  19. Singh, G., & Chaturvedi, A. K. (2024). A cost, time, energy-aware workflow scheduling using adaptive PSO algorithm in a cloud–fog environment. Computing, 106(10), 3279–3308.
  20. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
  21. Xue, F., Zhou, Y., & Li, J. (2020). Improved particle swarm optimisation for large-scale scheduling problems. Applied Soft Computing, 92, 106–122.
  22. Zeng, C., Wang, X., Zeng, R., Li, Y., Shi, J., & Huang, M. (2024). Joint optimization of multi-dimensional resource allocation and task offloading for QoE enhancement in Cloud-Edge-End collaboration. Future Generation Computer Systems, 155, 121–131.
  23. Zhang, H., Guo, S., & Zhang, J. (2021). Digital twin-driven resource optimisation in intelligent computing systems. Future Generation Computer Systems, 123, 1–12.