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CluM: A Clustering–Cum–Markov model for resource prediction in a data center Cover

CluM: A Clustering–Cum–Markov model for resource prediction in a data center

Open Access
|Sep 2025

References

  1. Bi, J., Li, S., Yuan, H. and Zhou, M. (2021). Integrated deep learning method for workload and resource prediction in cloud systems, Neurocomputing 424: 35–48.
  2. Bi, J., Yuan, H., Zhou, M. and Liu, Q. (2019). Time-dependent cloud workload forecasting via multi-task learning, IEEE Robotics and Automation Letters 4(3): 2401–2406.
  3. Chen, C., Wang, F., Pan, J., Xu, L. and Gao, H. (2024). Algorithm design for an online Berth allocation problem, Journal of Marine Science and Engineering 12(10): 1722.
  4. Chen, L., Zhang, W. and Ye, H. (2022). Accurate workload prediction for edge data centers: Savitzky–Golay filter, CNN and BILSTM with attention mechanism, Applied Intelligence 52(11): 13027–13042.
  5. Chen, T., Zhang, Y., Wang, X. and Giannakis, G.B. (2016). Robust workload and energy management for sustainable data centers, IEEE Journal on Selected Areas in Communications 34(3): 651–664.
  6. Chen, Z., Hu, J., Min, G., Zomaya, A.Y. and El-Ghazawi, T. (2019). Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning, IEEE Transactions on Parallel and Distributed Systems 31(4): 923–934.
  7. Gong, Z., Gu, X. and Wilkes, J. (2010). PRESS: Predictive elastic resource scaling for cloud systems, 2010 International Conference on Network and Service Management, Niagara Falls, Canada, pp. 9–16.
  8. Guan, Y., Xiao, W.-Q., Cheung, R.K. and Li, C.-L. (2002). A multiprocessor task scheduling model for Berth allocation: Heuristic and worst-case analysis, Operations Research Letters 30(5): 343–350.
  9. Gupta, S., Dileep, A.D. and Gonsalves, T.A. (2020). Online sparse BLSTM models for resource usage prediction in cloud datacentres, IEEE Transactions on Network and Service Management 17(4): 2335–2349.
  10. Hieu, N. T., Di Francesco, M. and Ylä-Jääski, A. (2017). Virtual machine consolidation with multiple usage prediction for energy-efficient cloud data centers, IEEE Transactions on Services Computing 13(1): 186–199.
  11. Jheng, J.-J., Tseng, F.-H., Chao, H.-C. and Chou, L.-D. (2014). A novel VM workload prediction using grey forecasting model in cloud data center, International Conference on Information Networking (ICOIN2014), Phuket, Thailand, pp. 40–45.
  12. Khan, T., Tian, W., Ilager, S. and Buyya, R. (2022). Workload forecasting and energy state estimation in cloud data centres: ML-centric approach, Future Generation Computer Systems 128: 320–332.
  13. Kim, S.-R., Prasad, A.K., El-Askary, H., Lee, W.-K., Kwak, D.-A., Lee, S.-H. and Kafatos, M. (2014). Application of the Savitzky–Golay filter to land cover classification using temporal modis vegetation indices, Photogrammetric Engineering & Remote Sensing 80(7): 675–685.
  14. Liu, R., Sun, W. and Hu, W. (2020). Workload based geo-distributed data center planning in fast developing economies, IEEE Access 8: 224269–224282.
  15. Lu, Y., Liu, L., Panneerselvam, J., Zhai, X., Sun, X. and Antonopoulos, N. (2019). Latency-based analytic approach to forecast cloud workload trend for sustainable datacenters, IEEE Transactions on Sustainable Computing 5(3): 308–318.
  16. Miroshin, R. (2016). Special solutions of the Chapman–Kolmogorov equation for multidimensional-state Markov processes with continuous time, Vestnik St. Petersburg University: Mathematics 49: 122–129.
  17. Nguyen, H., Shen, Z., Gu, X., Subbiah, S. and Wilkes, J. (2013). AGILE: Elastic distributed resource scaling for infrastructure-as-a-service, 10th International Conference on Autonomic Computing (ICAC’13), San Jose, USA, pp. 69–82.
  18. Qazi, K., Li, Y. and Sohn, A. (2014). Workload prediction of virtual machines for harnessing data center resources, 2014 IEEE 7th International Conference on Cloud Computing, Anchorage, USA, pp. 522–529.
  19. Różycki, R., Waligóra, G. and Węglarz, J. (2016). Scheduling preemptable jobs on identical processors under varying availability of an additional continuous resource, International Journal of Applied Mathematics and Computer Science 26(3): 693–706, DOI: 10.1515/amcs-2016-0048.
  20. Sanjalawe, Y., Al-E’mari, S., Fraihat, S. and Makhadmeh, S. (2025). AI-driven job scheduling in cloud computing: a comprehensive review, Artificial Intelligence Review 58(7): 197.
  21. Santos, J., Wang, C., Wauters, T. and De Turck, F. (2023). DIKTYO: Network-aware scheduling in container-based clouds, IEEE Transactions on Network and Service Management 20(4): 4461–4477.
  22. Saxena, D., Kumar, J., Singh, A.K. and Schmid, S. (2023). Performance analysis of machine learning centered workload prediction models for cloud, IEEE Transactions on Parallel and Distributed Systems 34(4): 1313–1330.
  23. Singh, A. K., Saxena, D., Kumar, J. and Gupta, V. (2021). A quantum approach towards the adaptive prediction of cloud workloads, IEEE Transactions on Parallel and Distributed Systems 32(12): 2893–2905.
  24. Sun, X., Ansari, N. and Wang, R. (2016). Optimizing resource utilization of a data center, IEEE Communications Surveys & Tutorials 18(4): 2822–2846.
  25. Vasiliu, L., Pop, F., Negru, C., Mocanu, M., Cristea, V. and Kolodziej, J. (2017). A hybrid scheduler for many task computing in big data systems, International Journal of Applied Mathematics and Computer Science 27(2): 385–399, DOI: 10.1515/amcs-2017-0027.
  26. Wilkes, J. (2019). Clusterdata 2019 traces, https://github.com/google/cluster-data/blob/master/ClusterData2019.md.
  27. Xiao, Z., Song, W. and Chen, Q. (2012). Dynamic resource allocation using virtual machines for cloud computing environment, IEEE Transactions on Parallel and Distributed Systems 24(6): 1107–1117.
  28. Xu, Z., Gong, Y., Zhou, Y., Bao, Q. and Qian, W. (2024). Enhancing kubernetes automated scheduling with deep learning and reinforcement techniques for large-scale cloud computing optimization, 9th International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2024), Changchun, China, pp. 1595–1600.
  29. Zhuang, X., Wang, Y., Hao, S. and Wang, X. (2025). Enhancing graph topology and clustering quality: A modularity-guided approach, International Conference on Pattern Recognition, Kolkata, India, pp. 131–142.
DOI: https://doi.org/10.61822/amcs-2025-0038 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 535 - 545
Submitted on: Jul 19, 2024
Accepted on: Jun 10, 2025
Published on: Sep 8, 2025
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Madhupriya Govindarajan, Mercy Shalinie Selvaraj, Nagarathna Ravi, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.