DOI: https://doi.org/10.61435/ijred.2026.62635
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Flexible scheduling strategy and optimization algorithm for container resources in power environment

Hainan Power Grid Co., Ltd., Haikou, 570100, China

Received: 17 Apr 2026; Revised: 25 Jul 2026; Accepted: 15 Aug 2026; Available online: 9 Sep 2026; Published: 1 Nov 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2026 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

The power business load has significant periodicity and suddenness characteristics, and has high requirements for operational reliability. Traditional container resource static allocation methods and general elastic scheduling strategies are difficult to achieve efficient and energy-saving resource utilization while ensuring service quality. To address the above issues, research and analyze the load patterns and various constraints of typical power business scenarios, construct a mixed integer programming model with the goal of minimizing service response time, resource fragmentation rate, and system energy consumption, and propose a hierarchical elastic scheduling framework. This framework integrates a threshold based passive scaling mechanism and a pre scheduling mechanism based on bidirectional long short-term memory network load prediction. For high proportion new energy power scenarios, the wind and photovoltaic output cycle encoding is embedded into the model, and grid load constraints are introduced to achieve deep coupling between power system energy flow and container computing power flow. In a typical mixed load scenario, comparative experiments were conducted with Kubernetes' default horizontal container auto scaling and classic best fit algorithms. The results showed that the optimized scheduling scheme proposed in this paper reduced the average response time of applications by 31.2%, increased the average resource utilization rate of the cluster from 58.72% to 86.63%, and controlled the service violation rate from 8.55% to below 1.00%; Through the integration of intelligent nodes, the overall energy consumption of the system has decreased by about 22.3%. This article provides effective theoretical methods and engineering practice references for the dynamic management and optimization of cloud native infrastructure resources in the power industry.

Keywords: Container scheduling; Elastic scaling; Resource optimization; Multi-objective particle swarm optimization; Kubernetes.

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Article Info
Section: Original Research Article
Language : EN
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  1. Aruna, K., & Pradeep, G. (2024). Container-to-fog service integration using the dis-lc algorithm. International Journal of Computer Network and Information Security, 16(1), 85-96. https://doi.org/10.5815/ijcnis.2024.01.07
  2. Akkepalli, S. (2025). A Novel Framework of Anomaly-Based Network Intrusion Detection using Hybrid CNN, Bi-LSTM Deep Learning Techniques. Journal of Information Systems Engineering & Management, 10(19), 247-254. https://doi.org/10.52783/jisem.v10i19s.3015
  3. Baldouski, D., Krész, M., & Dávid, B. (2025). Scheduling truck arrivals for efficient container flow management in port logistics. Central European journal of operations research, 33(3), 791-818. https://doi.org/10.1007/s10100-025-00976-x
  4. Cen, H. (2023). Assembly Sequence and Assembly Path Planning of Robot Automation Products Based on Discrete Particle Swarm Optimization. International Journal of Vehicle Structures and Systems, 15(3), 400-408. https://doi.org/10.4273/ijvss.15.3.20
  5. Cao, R., Zhang, P., Wu, Y., Liu, J., & Su, H. (2025). Adaptive container scheduling based on reinforcement learning in kubernetes. CCF Transactions on High Performance Computing, 7(3), 291-304. https://doi.org/10.1007/s42514-025-00223-4
  6. Damayanti, A., Sarto, S., & Sediawan, W. (2020). Biohydrogen Production by Reusing Immobilized Mixed Culture in Batch System. International Journal of Renewable Energy Development, 9(1), 37-42. https://doi.org/10.14710/ijred.9.1.37-42
  7. Du, B., Guo, S., Guo, J., Sun, X., Wang, K., & Yang, Z. (2024). A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly. International Journal of Production Research, 62(13), 4809-4830. https://doi.org/10.1080/00207543.2023.2280696
  8. Duan, J., Xiong, Z., & Li, L. (2025). Integrated sustainable scheduling in u-shaped automated container terminal considering collection–distribution operations and adaptive partitioning strategies. Transportation Research Record, 2679(7), 353-384. https://doi.org/10.1177/03611981251327218
  9. Deng, W., Zhu, L., Shen, Y., Zhou, C., Guo, J. , & Cheng, Y. (2025). A novel multi-objective optimized dag task scheduling strategy for fog computing based on container migration mechanism. Wireless Networks, 31(2), 1005-1019. https://doi.org/10.1007/s11276-024-03811-4
  10. Gill, S. S., Kumar, M., Samriya, J. K., & Dubey, K. (2023). QoS‐aware resource scheduling using whale optimization algorithm for microservice applications. Software: Practice and Experience, 54(4), 546-565. https://doi.org/10.1002/spe.3211
  11. Guo, J., Li, Z., Lou, J., Tang, Z., Wang, T., & Jia, W. (2025). Online Layer-Aware Joint Request Scheduling, Container Placement, and Resource Provision in Edge Computing. IEEE Transactions on Services Computing, 18(1), 328-341. https://doi.org/10.1109/TSC.2024.3504237
  12. Han, Q., Shan, C., Gao, R., Liu, T., Yang, Z., & Zhang, J. (2025). KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration Framework. IEEE Internet of Things Journal, 12(2), 2026-2042. https://doi.org/10.1109/JIOT.2024.3466231
  13. Hansen, F., Bayo, A., Ye, X., Zhang, Y., Du, X., & Li, J. (2024). Molecular spectral line data preprocessing container load grouping prediction algorithm based on EMD-LSTM. Journal of Applied Artificial Intelligence, 1(1), 68-84. https://doi.org/10.59782/aai.v1i1.279
  14. Hyder, M. F., Ahmed, S. H., Latif, M., Aslam, K., Rab, A. U., & Siddiqui, M. T. (2024). Towards Digital Forensics Investigation of WordPress Applications Running Over Kubernetes. IETE Journal of Research, 70(4), 3856-3871. https://doi.org/10.1080/03772063.2023.2195837
  15. Jiang, X., Song, B., Yan, H., & Yan, H. (2025). Optimization of Active Response Efficiency of Power Demand Side Based on Discrete Particle Swarm Optimization. Journal of Intelligent & Fuzzy Systems, 49(3), 822–835. https://doi.org/10.1177/18758967251353378
  16. Khan, S., Calheiros, R. N., Maurushat, A., & Momposhi, L. (2025). Next-Gen Threat Hunting: A Comparative Study of ML Models in Android Ransomware Detection. FinTech and Sustainable Innovation, 1, A3-A3. https://doi.org/10.47852/bonviewfsi52025685
  17. Li, K., Lin, W., Mo, R., Xu, M., & Zhong, H. (2025). A Cross-Workload Power Prediction Method Based on Transfer Gaussian Process Regression in Cloud Data Centers. IEEE Transactions on Cloud Computing, 13(3), 910-921. https://doi.org/10.1109/TCC.2025.3575790
  18. Li, K., Lin, W., Wu, H., Zhang, H., Shi, F., & Shen, W. (2025). Container Scheduling Strategy Based on Image Layer Reuse and Sequential Arrangement in Mobile Edge Computing. IEEE Transactions on Mobile Computing, 24(9), 8700-8713. https://doi.org/10.1109/TMC.2025.3557160
  19. Li, R., Liu, S., Wang, W., Zhong, S., Peng, Y., & Tian, Q. (2024). A graph-based approach for integrating massive data in container terminals with application to scheduling problem. International Journal of Production Research, 62(16), 5945-5965. https://doi.org/10.1080/00207543.2024.2304021
  20. Liu, Y., Wang, K. (2025). Cloud computing based green and low-carbon building energy consumption monitoring and forecasting. International Journal of Global Warming, 36(4):350-364. https://doi.org/10.1504/IJGW.2025.147643
  21. Li, Z., Lou, J., Wu, J., Guo, J., Tang, Z., & Shen, P., et al. (2024). Online container scheduling with fast function startup and low memory cost in edge computing. IEEE Transactions on Computers, 73(12), 2747-2760. https://doi.org/10.1109/TC.2024.3441836
  22. Kafhali, S. E. . (2026). A survey of adaptive scheduling techniques, goals, and challenges in kubernetes. Archives of Computational Methods in Engineering, 33(4), 6047-6070. https://doi.org/10.1007/s11831-026-10497-8
  23. Mandal, R., Mondal, M. K., Banerjee, S., Srivastava, G., Alnumay, W., & Ghosh, U., et al. (2023). MECpVmS: an SLA aware energy-efficient virtual machine selection policy for green cloud computing. Cluster computing, 2023. https://doi.org/10.1007/s10586-022-03684-2
  24. Neugebauer, J., Ahmet Cürebal, Heilig, L., & Vo, S. (2026). Straddle carrier routing optimization at container terminals utilizing telemetry‐based prediction. International Transactions in Operational Research, 34(1), 429-459. https://doi.org/10.1111/itor.70186
  25. Nguyen, V. S., Pham, Q. D., & Huynh, T. T. (2024). Modelling and solving a real-world truck-trailer scheduling problem in container transportation with separate moving objects. Opsearch, 61(2), 628-661
  26. Petrosyan, D. A. (2025). Scheduling ml and hpc jobs with shoc platform over kubernetes. Physics of Particles and Nuclei, 56(6), 1581-1585. https://doi.org/10.1134/S106377962570090X
  27. Peng, W., Tang, Z., Guo, J., Lou, J., Wang, T., & Jia, W. (2025). LR2Scheduler: layer-aware, resource-balanced, and request-adaptive container scheduling for edge computing. CCF Transactions on Pervasive Computing and Interaction, 7(4), 377-391. https://doi.org/10.1007/s42486-025-00186-z
  28. Park, S., & Bahn, H. (2024). Prediction Techniques of Container Workload Considering the Resource Usage Characteristics of CPU and Memory. The Journal of the Institute of Internet, Broadcasting and Communication, 24(6), 115-120. https://doi.org/10.7236/JIIBC.2024.24.6.115
  29. Poojitha, S. A., & Ravindranath, K. (2025). Quality aware batch scheduling of containers in cloud computing environment. International Journal of Information Technology, 17(2), 1155-1163. https://doi.org/10.1007/s41870-024-02331-w
  30. Roi, H. V., You, S. S., Kim, H. S., Long, L. N. B., Cuong, T. N., & Nguyen, D. A., et al. (2025). Heuristic-assisted deep learning for integrated scheduling of multiple equipment in automated container terminals. Maritime economics & logistics, 27(4), 786-818. https://doi.org/10.1057/s41278-025-00327-2
  31. Rezaei, P., Hadid, M., Elomri, A., & Aydin, N. (2025). Enhancing maritime container logistics through optimized repair scheduling. Procedia Computer Science, 274(3), 248-257. https://doi.org/10.1016/j.procs.2025.12.025
  32. Sanaa, A., Imane, T., & Mohamed, M. (2025). Quay crane scheduling in container terminals using a hybrid genetic algorithm. Journal of Computer Science, 21(1), 197-202. https://doi.org/10.3844/jcssp.2025.197.202
  33. Sarma, S. K. (2023). Metaheuristic based auto-scaling for microservices in cloud environment: a new container-aware application scheduling. International Journal of Pervasive Computing and Communications, 19(1), 74-96. https://doi.org/10.1108/IJPCC-12-2020-0213
  34. Shakya, S., Tripathi, P. (2024). Using light weight container a mesh based dynamic allocation task scheduling algorithm for cloud with IoT network. International Journal of Information Technology, 16(5), 2847-2861. https://doi.org/10.1007/s41870-024-01772-7
  35. Sharma, S., & Vishwakarma, R. (2025). An efficient container scheduling framework for resource allocation in a cloud computing environments. Communications on Applied Electronics, 7(40), 39-48. https://doi.org/10.5120/cae2025652909
  36. Subramanyam, R. V. B., Padhi, S. (2023). Efficient Renewable Energy-Based Geographical Load Balancing Algorithms for Green Cloud Computing. International Journal of Web and Grid Services, 19(4), 401-426. https://doi.org/10.1504/ijwgs.2023.10058557
  37. Tang, Z., Mou, F., Lou, J., Jia, W., Wu, Y., & Zhao, W. (2024). Joint resource overbooking and container scheduling in edge computing. IEEE Transactions on Mobile Computing, 23(12), 10903-10917. https://doi.org/10.1109/TMC.2024.3386936
  38. Thallapally, N. (2024). Scalable Application Deployment with Docker and Kubernetes. Journal of Computer Science and Technology Studies, 6(1), 249-257. https://doi.org/10.32996/jcsts.2024.6.1.29
  39. Vinayak, P. S., Mallick, S. S., Jagarlamudi, L., Chakraborty, A., & Simmhan, Y. (2025). Carl: cost-optimized online container placement on vms using adversarial reinforcement learning. IEEE Transactions on Cloud Computing, 13(1), 321-335. https://doi.org/10.1109/TCC.2025.3528446
  40. Wellalage, A., Fackrell, M., & Zhang, L. (2024). A Monte Carlo simulation-based simulated annealing algorithm for predicting the minimum staffing requirement at a blood donor centre. Annals of Operations Research, 342(3), 1945-1990. https://doi.org/10.1007/s10479-023-05297-3
  41. Wu, J., Guo, J. Tang, Z., Luo, C., Wang, T., & Jia, W. (2025). Sequence-aware online container scheduling with reinforcement learning in parked vehicle edge computing. Vehicular Technology, IEEE Transactions, 74(8), 12921-12934. https://doi.org/10.1109/TVT.2025.3554595

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