Development of a predictive adaptive resource reallocation method with critical process dispatching in information systems on mobile platforms

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.350796

Keywords:

information system, predictive-adaptive control, critical process survivability, dynamic resource reservation

Abstract

This work investigates the process of real-time resource management of an information system on a mobile platform under intermittent connectivity, destructive influences, and nonstationary resource availability.

The scientific task relates to the fact that forecast errors and the inertia of platform reconfiguration can induce oscillatory resource redistribution, causing critical processes to intermittently lose the minimum required resource at each control time-step.

A time-step-based predictive adaptive redistribution method with dynamic reservation has been devised in this study. The method introduces an operational survivability constraint. It is formulated as a requirement to maintain a minimum guaranteed resource profile for critical processes. The method adapts the reservation volume according to the assessed reliability of the current forecast. It also constrains the frequency of reconfigurations within a sliding window. These constraints are combined with dispatching of critical processes by urgency and allowable delay. By combining forecast-adaptive reservation with inertia-consistent reconfiguration constraints, the control loop reduces the amplitude and the cumulative intensity of reconfigurations. In particular, under bursty critical workload, the maximum reconfiguration step decreases by about 52%, while the cumulative magnitude of profile changes decreases by about 14%. These effects are explained by the fact that reserved resources compensate for forecast degradation, whereas reconfiguration constraints prevent abrupt control actions and stabilize time-step allocation.

The results could be implemented to build resource management information systems for robotic platforms, sensor networks, as well as mobile systems under intermittent connectivity and real-time resource degradation.

Author Biographies

Vitalii Tkachov, Kharkiv National University of Radio Electronics

PhD

Department of Electronic Computers

Ihor Ruban, Kharkiv National University of Radio Electronics

Doctor of Technical Sciences

Department of Electronic Computers

References

  1. Fesenko, H., Illiashenko, O., Kharchenko, V., Leichenko, K., Sachenko, A., Scislo, L. (2024). Methods and Software Tools for Reliable Operation of Flying LiFi Networks in Destruction Conditions. Sensors, 24 (17), 5707. https://doi.org/10.3390/s24175707
  2. Dodonov, O., Gorbachyk, O., Kuznietsova, M. (2023). Critical Infrastructure Resilience and Cybersecurityof Information Management Systems. Selected Papers of the XXIII International Scientific and Practical Conference "Information Technologies and Security" (ITS 2023). Available at: https://ceur-ws.org/Vol-3887/paper1.pdf
  3. Xia, X., Fattah, S. M. M., Babar, M. A. (2023). A Survey on UAV-Enabled Edge Computing: Resource Management Perspective. ACM Computing Surveys, 56 (3), 1–36. https://doi.org/10.1145/3626566
  4. Churyumov, G., Tokarev, V., Tkachov, V., Partyka, S. (2018). Scenario of Interaction of the Mobile Technical Objects in the Process of Transmission of Data Streams in Conditions of Impacting the Powerful Electromagnetic Field. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 183–186. https://doi.org/10.1109/dsmp.2018.8478539
  5. Kvasnikov, V., Ornatskyi, D., Graf, M., Shelukha, O. (2021). Designing a computerized information processing system to build a movement trajectory of an unmanned aircraft. Eastern-European Journal of Enterprise Technologies, 1 (9 (109)), 33–42. https://doi.org/10.15587/1729-4061.2021.225501
  6. Dodonov, O., Jiang, B., Dodonov, V. (2019). Survivability Mechanisms of Complex Computer Systems Based on Common Information Space. Computer Engineering, 45 (7), 41–45. https://doi.org/10.19678/j.issn.1000-3428.0053440
  7. Yu, C., Rosendo, A. (2021). Risk-Aware Model-Based Control. Frontiers in Robotics and AI, 8. https://doi.org/10.3389/frobt.2021.617839
  8. Ruban, I. V., Tkachov, V. M. (2025). A method for synthesizing index policies to ensure the survivability of a mobile platform-based information system. Applied Aspects of Information Technology, 8 (4), 424–441. https://doi.org/10.15276/aait.08.2025.27
  9. Dahiya, A., Akbarzadeh, N., Mahajan, A., Smith, S. L. (2022). Scalable Operator Allocation for Multirobot Assistance: A Restless Bandit Approach. IEEE Transactions on Control of Network Systems, 9 (3), 1397–1408. https://doi.org/10.1109/tcns.2022.3153872
  10. Zeng, Y., Wu, L., Li, J., Zhuang, X., Wu, C. (2025). Resilient Task Allocation for UAV Swarms: A Bilevel PSO-ILP Optimization Approach. Drones, 9 (9), 623. https://doi.org/10.3390/drones9090623
  11. Bali, A., Houm, Y. E., Gherbi, A., Cheriet, M. (2024). Automatic data featurization for enhanced proactive service auto-scaling: Boosting forecasting accuracy and mitigating oscillation. Journal of King Saud University - Computer and Information Sciences, 36 (2), 101924. https://doi.org/10.1016/j.jksuci.2024.101924
  12. Kuchuk, N., Tkachov, V. (2022). Self-healing Systems Modelling. Advances in Self-Healing Systems Monitoring and Data Processing, 57–111. https://doi.org/10.1007/978-3-030-96546-4_2
  13. Zhang, X., Debroy, S. (2023). Resource Management in Mobile Edge Computing: A Comprehensive Survey. ACM Computing Surveys, 55 (13s), 1–37. https://doi.org/10.1145/3589639
  14. Djigal, H., Xu, J., Liu, L., Zhang, Y. (2022). Machine and Deep Learning for Resource Allocation in Multi-Access Edge Computing: A Survey. IEEE Communications Surveys & Tutorials, 24 (4), 2449–2494. https://doi.org/10.1109/comst.2022.3199544
  15. Ismail, A. A., Khalifa, N. E., El-Khoribi, R. A. (2025). A survey on resource scheduling approaches in multi-access edge computing environment: a deep reinforcement learning study. Cluster Computing, 28 (3). https://doi.org/10.1007/s10586-024-04893-7
  16. Zhan, W., Luo, C., Wang, J., Wang, C., Min, G., Duan, H., Zhu, Q. (2020). Deep-Reinforcement-Learning-Based Offloading Scheduling for Vehicular Edge Computing. IEEE Internet of Things Journal, 7 (6), 5449–5465. https://doi.org/10.1109/jiot.2020.2978830
  17. Hu, X., Huang, Y. (2022). Deep reinforcement learning based offloading decision algorithm for vehicular edge computing. PeerJ Computer Science, 8, e1126. https://doi.org/10.7717/peerj-cs.1126
  18. Luo, Q., Li, C., Luan, T. H., Shi, W. (2020). Collaborative Data Scheduling for Vehicular Edge Computing via Deep Reinforcement Learning. IEEE Internet of Things Journal, 7 (10), 9637–9650. https://doi.org/10.1109/jiot.2020.2983660
  19. Qu, G., Wu, H., Li, R., Jiao, P. (2021). DMRO: A Deep Meta Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing. IEEE Transactions on Network and Service Management, 18 (3), 3448–3459. https://doi.org/10.1109/tnsm.2021.3087258
  20. Chang, J., Wang, J., Li, B., Zhao, Y., Li, D. (2024). Attention-Based Deep Reinforcement Learning for Edge User Allocation. IEEE Transactions on Network and Service Management, 21 (1), 590–604. https://doi.org/10.1109/tnsm.2023.3292272
  21. Shlash Mohammad, A. A., Shelash Al-Hawary, S. I., Hindieh, A., Vasudevan, A., Mohd Al-Shorman, H., Al-Adwan, A. S. et al. (2025). Intelligent Data-Driven Task Offloading Framework for Internet of Vehicles Using Edge Computing and Reinforcement Learning. Data and Metadata, 4, 521. https://doi.org/10.56294/dm2025521
  22. Ding, Y., Li, K., Liu, C., Li, K. (2022). A Potential Game Theoretic Approach to Computation Offloading Strategy Optimization in End-Edge-Cloud Computing. IEEE Transactions on Parallel and Distributed Systems, 33 (6), 1503–1519. https://doi.org/10.1109/tpds.2021.3112604
  23. Chen, Y., Zhao, J., Wu, Y., Huang, J., Shen, X. (2024). QoE-Aware Decentralized Task Offloading and Resource Allocation for End-Edge-Cloud Systems: A Game-Theoretical Approach. IEEE Transactions on Mobile Computing, 23 (1), 769–784. https://doi.org/10.1109/tmc.2022.3223119
  24. Munir, Md. S., Abedin, S. F., Tran, N. H., Han, Z., Huh, E.-N., Hong, C. S. (2021). Risk-Aware Energy Scheduling for Edge Computing With Microgrid: A Multi-Agent Deep Reinforcement Learning Approach. IEEE Transactions on Network and Service Management, 18 (3), 3476–3497. https://doi.org/10.1109/tnsm.2021.3049381
  25. Zhou, S., Ali, A., Al-Fuqaha, A., Omar, M., Feng, L. (2024). Robust Risk-Sensitive Task Offloading for Edge-Enabled Industrial Internet of Things. IEEE Transactions on Consumer Electronics, 70 (1), 1403–1413. https://doi.org/10.1109/tce.2023.3323146
  26. Mao, S., He, S., Wu, J. (2021). Joint UAV Position Optimization and Resource Scheduling in Space-Air-Ground Integrated Networks With Mixed Cloud-Edge Computing. IEEE Systems Journal, 15 (3), 3992–4002. https://doi.org/10.1109/jsyst.2020.3041706
  27. Xu, Z., Yu, Q., Yang, X. (2024). Joint Resource Allocation Optimization in Space–Air–Ground Integrated Networks. Drones, 8 (4), 157. https://doi.org/10.3390/drones8040157
  28. Zhang, J., Ning, Z., Waqas, M., Alasmary, H., Tu, S., Chen, S. (2024). Hybrid Edge-Cloud Collaborator Resource Scheduling Approach Based on Deep Reinforcement Learning and Multiobjective Optimization. IEEE Transactions on Computers, 73 (1), 192–205. https://doi.org/10.1109/tc.2023.3326977
  29. Taghinezhad-Niar, A., Taheri, J. (2024). Security, Reliability, Cost, and Energy-Aware Scheduling of Real-Time Workflows in Compute-Continuum Environments. IEEE Transactions on Cloud Computing, 12 (3), 954–965. https://doi.org/10.1109/tcc.2024.3426282
  30. Nkongolo, M. (2023). Using ARIMA to Predict the Growth in the Subscriber Data Usage. Eng, 4 (1), 92–120. https://doi.org/10.3390/eng4010006
  31. Al-Absi, M. A., Fu, R., Kim, K.-H., Lee, Y.-S., Al-Absi, A. A., Lee, H.-J. (2021). Tracking Unmanned Aerial Vehicles Based on the Kalman Filter Considering Uncertainty and Error Aware. Electronics, 10 (24), 3067. https://doi.org/10.3390/electronics10243067
  32. Park, M.-J., Yang, H.-S. (2024). Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI. Sensors, 24 (22), 7205. https://doi.org/10.3390/s24227205
  33. Zhang, S., Lu, J., Zhao, H. (2024). Deep Network Approximation: Beyond ReLU to Diverse Activation Functions. Journal of Machine Learning Research, 25. Available at: https://jmlr.org/papers/volume25/23-0912/23-0912.pdf
  34. Wang, W., Mao, C., Zhao, S., Cao, Y., Yi, Y., Chen, S., Liu, Q. (2021). A Smart Semipartitioned Real‐Time Scheduling Strategy for Mixed‐Criticality Systems in 6G‐Based Edge Computing. Wireless Communications and Mobile Computing, 2021 (1). https://doi.org/10.1155/2021/6663199
  35. Tkachov, V., Ruban, I. (2025). Experimental Dataset (S1–S5): Resource Reallocation and Critical Process Dispatching on Mobile Platforms. Zenodo. https://doi.org/10.5281/zenodo.18056221
  36. Jierula, A., Wang, S., OH, T.-M., Wang, P. (2021). Study on Accuracy Metrics for Evaluating the Predictions of Damage Locations in Deep Piles Using Artificial Neural Networks with Acoustic Emission Data. Applied Sciences, 11 (5), 2314. https://doi.org/10.3390/app11052314
  37. Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development, 15 (14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022
Development of a predictive adaptive resource reallocation method with critical process dispatching in information systems on mobile platforms

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Published

2026-02-27

How to Cite

Tkachov, V., & Ruban, I. (2026). Development of a predictive adaptive resource reallocation method with critical process dispatching in information systems on mobile platforms. Eastern-European Journal of Enterprise Technologies, 1(3 (139), 6–23. https://doi.org/10.15587/1729-4061.2026.350796

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Section

Control processes