ARIMA Forecasting and Linear Programming with Simulated Annealing for Adaptive Network Traffic Scheduling

Junfeng Dan; Yanhao Fan; Guo Chen; Zicheng Meng

doi:10.54097/c19srf59

Authors

Junfeng Dan
Yanhao Fan
Guo Chen
Zicheng Meng

DOI:

https://doi.org/10.54097/c19srf59

Keywords:

ARIMA Time-Series Forecasting, Linear Programming Optimization, Simulated Annealing Heuristic, Entropy-Weighted TOPSIS Evaluation, Multi-Objective Resource Scheduling, Stochastic Perturbation Robustness

Abstract

Adaptive resource scheduling in distributed network systems requires accurate forecasting of incoming traffic, principled allocation of workload across processing paths, and disciplined selection of high-impact nodes for capacity expansion. This paper develops a four-stage framework that integrates ARIMA time-series forecasting, single-objective and multi-objective linear programming, simulated annealing optimization, and entropy-weighted TOPSIS evaluation to handle the full scheduling pipeline. Stationarity is verified through ADF testing before fitting an ARIMA model that produces 31-day-ahead forecasts of per-path traffic volume with calibrated residuals. The forecasts feed a linear programming formulation that minimizes total operational cost subject to per-node utilization caps of 100%, solved through simulated annealing with adaptive cooling schedule. The multi-objective extension introduces auxiliary capacity reallocation goals and identifies one additional path 3→1 in the recommended topology. Entropy-weighted TOPSIS evaluation across multiple importance criteria selects the top three nodes 10, 14, and 4 and the top three paths 14→8, 14→9, and 36→4 for capacity expansion. Sensitivity analysis under random perturbation injection confirms that the proposed scheduling policy retains feasible utilization profiles across all perturbation scales tested, demonstrating robust performance under stochastic traffic variation typical of operational network deployments.

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References

[1] Kwon, J. H., Kim, H. J., & Lee, S. (2024). Optimizing traffic scheduling in autonomous vehicle networks using machine learning techniques and time-sensitive networking. Electronics, 13(14), 2837. https://doi.org/10.3390/electronics13142837

[2] 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), 184. https://doi.org/10.1007/s10586-024-04822-9

[3] Ayadi, M., Masmoudi, N., Almuqren, L., Alshahrani, H. S., & Aljohani, R. O. (2025). Designing a novel CNN-LSTM-based model for Arabic handwritten character recognition for the visually impaired person. Journal of Disability Research, 4(1), 20240080. https://doi.org/10.36348/jdr.2025.v04i01.080

[4] Khan, A., Ullah, F., Shah, D., Khan, M. H., Ali, S., & Tahir, M. (2025). EcoTaskSched: A hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments. Scientific Reports, 15(1), 12296. https://doi.org/10.1038/s41598-025-96022-7

[5] Vullam, N. R., Geetha, G., Rao, N., Vellela, S. S., Rao, T. S., Thommandru, R., & Rao, K. N. S. (2025). Optimized multitask scheduling in cloud computing using advanced machine learning techniques. In 2025 International Conference on Intelligent Control, Computing and Communications (IC3) (pp. 410–415). https://doi.org/10.1109/IC362328.2025.10945678

[6] Wu, B., Cai, Z., Wu, W., & Yin, X. (2023). AoI-aware resource management for smart health via deep reinforcement learning. IEEE Access, 11, 81180–81195. https://doi.org/10.1109/ACCESS.2023.3300872

[7] Alhassan, M. A. M., & Yılmaz, E. (2025). Evaluating YOLOv4 and YOLOv5 for enhanced object detection in UAV-based surveillance. Processes, 13(1), 254. https://doi.org/10.3390/pr13010254

[8] Wu, B., & Wu, W. (2023). Model-free cooperative optimal output regulation for linear discrete-time multi-agent systems using reinforcement learning. Mathematical Problems in Engineering, 2023, 6350647. https://doi.org/10.1155/2023/6350647

[9] Sherly, A., Christo, M. S., & Elizabeth, J. V. (2025). A hybrid approach to time series forecasting: Integrating ARIMA and prophet for improved accuracy. Results in Engineering, 27, 105703. https://doi.org/10.1016/j.rineng.2025.105703

[10] Wu, B., Huang, J., & Yu, S. (2026). "X of Information" continuum: A survey on AI-driven multi-dimensional metrics for next-generation networked systems. IEEE Communications Surveys & Tutorials, 28, 5307–5344. https://doi.org/10.1109/COMST.2026.3546219

[11] Reddy, B. D. K., Naik, J. S., Kumar, S. V., Kumar, S., Haritha, G., & Reddy, M. R. (2025). A methodological review on time series forecasting by using ARIMA. In International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024) (pp. 709–719). https://doi.org/10.1063/5.0212345

[12] Singh, A., Tripathi, T., Ranjan, R., & Tiwari, A. K. (2025). Time series forecasting of infant mortality rate in India using Bayesian ARIMA models. BMC Public Health, 25(1), 2855. https://doi.org/10.1186/s12889-025-14215-9

[13] Trigka, M., & Dritsas, E. (2025). A comprehensive survey of machine learning techniques and models for object detection. Sensors, 25(1), 214. https://doi.org/10.3390/s25010214

[14] Abduljabbar, R., Dia, H., & Liyanage, S. (2025). Machine learning traffic flow prediction models for smart and sustainable traffic management. Infrastructures, 10(7), 155. https://doi.org/10.3390/infrastructures10070155

[15] Xiuwu, Y., Shiqi, J., & Yong, L. (2025). Non-uniform WSN clustering routing protocol based on non-cooperative game. Wireless Personal Communications, 140(1), 561–590. https://doi.org/10.1007/s11277-024-11623-9

[16] Sattarzadeh, A. R., Kutadinata, R. J., Pathirana, P. N., & Huynh, V. T. (2025). A novel hybrid deep learning model with ARIMA Conv-LSTM networks and shuffle attention layer for short-term traffic flow prediction. Transportmetrica A: Transport Science, 21(1), 2236724. https://doi.org/10.1080/23249935.2025.2236724

[17] Naheliya, B., Redhu, P., & Kumar, K. (2025). A review on developments in evolutionary computation approaches for road traffic flow prediction. Archives of Computational Methods in Engineering, 32(3), 1499–1523. https://doi.org/10.1007/s11831-024-09998-7

[18] Wu, B., Huang, J., Duan, Q., Dong, L., & Cai, Z. (2025). Enhancing vehicular platooning with wireless federated learning: A resource-aware control framework. IEEE/ACM Transactions on Networking, 33(1), 1–16. https://doi.org/10.1109/TNET.2024.3421123

[19] Brun, A., Feron, E., Alam, S., & Delahaye, D. (2025). Schedule optimization and staff allocation for airport security checkpoints using guided simulated annealing and integer linear programming. Journal of Air Transport Management, 124, 102746. https://doi.org/10.1016/j.jairtraman.2025.102746

[20] Wu, B., Huang, J., & Duan, Q. (2025). FedTD3: An accelerated learning approach for UAV trajectory planning. In Proceedings of the International Conference on Wireless Artificial Intelligent Computing Systems and Applications (WASA) (pp. 13–24). https://doi.org/10.1109/WASA62101.2025.10897654

[21] Rozzi, E., Grimaldi, A., Minuto, F. D., & Lanzini, A. (2025). Model complexity and optimization trade-offs in the design and scheduling of hybrid hydrogen-battery systems. Energy Conversion and Management, 344, 120306. https://doi.org/10.1016/j.enconman.2025.120306

[22] Bechar, A., Medjoudj, R., Elmir, Y., Himeur, Y., & Amira, A. (2025). Federated and transfer learning for cancer detection based on image analysis. Neural Computing and Applications, 37(4), 2239–2284. https://doi.org/10.1007/s00521-024-09876-3

[23] Wu, B., Huang, J., & Duan, Q. (2025). Real-time intelligent healthcare enabled by federated digital twins with AoI optimization. IEEE Network, 1–7. https://doi.org/10.1109/MNET.2025.3498761

[24] Giglioni, V., Poole, J., Mills, R., Venanzi, I., Ubertini, F., & Worden, K. (2025). Transfer learning in bridge monitoring: Laboratory study on domain adaptation for population-based SHM of multispan continuous girder bridges. Mechanical Systems and Signal Processing, 224, 112151. https://doi.org/10.1016/j.ymssp.2024.112151

[25] Pan, D., Wu, B.-N., Sun, Y.-L., & Xu, Y.-P. (2023). A fault-tolerant and energy-efficient design of a network switch based on a quantum-based nano-communication technique. Sustainable Computing: Informatics and Systems, 37, 100827. https://doi.org/10.1016/j.suscom.2023.100827

[26] Wu, B., Ding, Z., & Huang, J. (2026). A review of continual learning in edge AI. IEEE Transactions on Network Science and Engineering, 13, 6571–6588. https://doi.org/10.1109/TNSE.2026.3527891