Journal of Visual Computing logo

Articles

Vol. 1 No. 4 (2026)

Resource-Change Point Detection for Online Reconfiguration of Scientific Workflows

Submitted
July 27, 2026
Published
August 10, 2026

Abstract

The execution of large-scale scientific workflows in modern distributed computing environments faces unprecedented challenges due to inherent resource volatility, performance degradation, and multi-tenancy interference. Traditional static resource provisioning strategies fail to adequately accommodate the dynamic fluctuations of underlying computational and network capacities, leading to suboptimal workflow makespans and severe resource underutilization. This paper presents a comprehensive theoretical and methodological framework for resource-change point detection tailored specifically for the online reconfiguration of scientific workflows. By leveraging advanced time-series analysis and hypothesis testing on streaming monitoring data, the proposed approach identifies structural breaks in resource availability and performance metrics in real-time. Upon detection, a dynamic reconfiguration strategy is triggered, evaluating the cost-benefit ratio of potential adaptation actions such as task migration, horizontal scaling, and dynamic rescheduling. Extensive analysis using simulated and real-world scientific workload traces, including astronomical and genomic applications, demonstrates that the integration of online change point detection with adaptive reconfiguration mechanisms significantly mitigates the impact of resource anomalies. The proposed framework reduces detection latency while maintaining a rigorously bounded false positive rate, ultimately ensuring high-throughput and reliable execution of mission-critical scientific computational tasks.

References

  1. 1. Zhao, R., Tang, J., Zeng, W., Guo, Y., & Zhao, X. (2025). Towards human-like questioning: Knowledge base question generation with bias-corrected reinforcement learning from human feedback. Information Processing & Management, 62(3), 104044.
  2. 2. Zhao, R., Xu, D., Jian, S., Tan, T., Sun, X., & Zhang, W. (2023, July). Quadratic Exponential Decrease Roll-Back: An Efficient Gradient Update Mechanism in Proximal Policy Optimization. In 2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM) (pp. 65-70). IEEE.
  3. 3. Zhang, J., Shi, Y., Ma, Y., Xu, L., Yu, J., & Wang, J. (2023, June). Ikol: Inverse kinematics optimization layer for 3d human pose and shape estimation via gauss-newton differentiation. In Proceedings of the AAAI conference on artificial intelligence (Vol. 37, No. 3, pp. 3454-3462).
  4. 4. Liu, Y., Cai, Q., Wang, C., Yang, J., Fan, H., Dong, J., & Chen, S. (2024). Geometry-enhanced attentive multi-view stereo for challenging matching scenarios. IEEE Transactions on Circuits and Systems for Video Technology, 34(8), 7401-7416.
  5. 5. Sun, L., Hu, J., Li, M., & Peng, H. (2024, July). R-ode: Ricci curvature tells when you will be informed. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2594-2598).
  6. 6. Wang, Y., Zhang, S., Ye, J., Peng, H., & Sun, L. (2024, October). A mixed-curvature graph diffusion model. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 2482-2492).
  7. 7. Wang, Y., Wang, L., Li, V., & Tu, Z. (2020, November). On the sparsity of neural machine translation models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1060-1066).
  8. 8. Gu, C., Zhang, W., Huang, Z., Kou, J., Liu, Z., Zhao, C., Liu, C., Zhang, L., Lin, W., Wang, Z., Deng, J., Xie, Y., Huang, G., Zhang, C., Lu, X., Wang, C., Zhang, Z., Yuan, H., Duan, X., & Fang, Y. (2024). LENS: Layers of evaluation of hallucination in GenAI systems. In 2024 7th International Conference on Universal Village (UV) (pp. 1–85). IEEE. https://doi.org/10.1109/UV63228.2024.11189150
  9. 9. Chiu, P. W., Yip, H. C., Xia, X. F., Chan, S. M., Ng, E. K., & Lau, J. Y. (2019). How I do it: Flexible 3‐D endoscope for endoscopic submucosal dissection. Digestive Endoscopy, 31(3), 323-328.
  10. 10. Zhang, Z., Yin, Z., Wen, J., Sun, L., Su, S., & Yu, P. S. (2021). DeepBlue: Bi-layered LSTM for tweet popularity estimation. IEEE Transactions on Knowledge and Data Engineering, 34(10), 4737-4752.
  11. 11. Sun, L., Zhang, Z., Zhang, J., Wang, F., Du, Y., Su, S., & Yu, P. S. (2020, November). Perfect: A hyperbolic embedding for joint user and community alignment. In 2020 IEEE International Conference on Data Mining (ICDM) (pp. 501-510). IEEE.
  12. 12. Lu, Y., Cao, C., Zhang, K., Zhang, L., Xue, X., & Tan, K. C. (2026, March). Exploring the Viability of Multi-Paradigm Transfer Optimization. In2026 6th International Symposium on Intelligent Robotics and Systems (ISoIRS)(pp. 1-5). IEEE.
  13. 13. Du, X., Chan, K. F., Xia, X., Chiu, P. W. Y., & Zhang, L. (2018, October). A magnetically-triggered soft capsule for on-demand mucus collection. In 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) (pp. 250-254). IEEE.
  14. 14. Wang, X., Suzuki, Y., Li, C. M., & Tanaka, S. (2024). Mechanical quality factor evaluation of damping film materials for polymer/PZT composite MEMS actuator. Journal of Microelectromechanical Systems, 33(5), 631-639.
  15. 15. Cao, C., Xue, X., Feng, L., & Tan, K. C. (2025, October). Why knowledge transfer works in oracle-based optimization: A brief analysis. In2025 International Conference on Machine Intelligence and Nature-Inspired Computing (MIND)(pp. 290-291). IEEE.
  16. 16. Du, S., Tao, Y., Shen, Y., Zhang, H., Shen, Y., Qiu, X., & Shi, C. (2025, March). Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA, and ChatGPT. In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE) (pp. 2290-2293). IEEE.
  17. 17. Cao, C., Zhang, K., Xue, X., Tan, K. C., Wang, J., Zhang, L., ... & Yan, X. (2024). Global and local search experience-based evolutionary sequential transfer optimization.IEEE Transactions on Evolutionary Computation,29(4), 1269-1283.
  18. 18. Liang, R., Xu, S., Xie, C., Chen, J., Ren, F., Yang, S., & Yabe, T. (2026). Abstain Mask Retain Core: Time Series Prediction by Adaptive Masking Loss with Representation Consistency. Advances in Neural Information Processing Systems, 38, 99445-99471.
  19. 19. Hu, H., Yang, Y., Zhao, Q., & Zhang, C. (2022, June). On the role of discount factor in offline reinforcement learning. In International conference on machine learning (pp. 9072-9098). PMLR.
  20. 20. Lin, C. C., Li, G., Hung, C. C., Kuo, Y. C., & Peng, W. C. (2026). Rethinking suspicious account identification from a perspective of machine learning ranking. International Journal of Machine Learning and Cybernetics, 17(3), 84.
  21. 21. Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, and Bin Liang. Offline reinforcement learning with value-based episodic memory. arXiv preprint arXiv:2110.09796, 2021b.
  22. 22. Zhang, T., Xu, J., Chen, J., Li, J., Xiao, Y., & Wang, Q. (2026). NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning. arXiv preprint arXiv:2608.04381.
  23. 23. Lin, Y. (2024). Design of urban road fault detection system based on artificial neural network and deep learning. Frontiers in neuroscience, 18, 1369832.
  24. 24. Chai, L., Zhang, J., Li, H., Cui, C., Jiang, J., Zheng, B., ... & Jiang, L. (2021). Investigation of thermomorphogenesis-related genes for a multi-silique trait in Brassica napus by comparative transcriptome analysis. Frontiers in Genetics, 12, 678804.
  25. 25. Yu, A., Huang, Y., & Xia, L. (2023). Efficient characterization of phase modulator based on Lyot-Sagnac interferometer. Measurement, 210, 112538.
  26. 26. Xia, Y., & Ma, J. (2022). Locality-guided global-preserving optimization for robust feature matching. IEEE Transactions on Image Processing, 31, 5093-5108.
  27. 27. Xia, Y., & Ma, J. (2025). Grid-guided sparse Laplacian consensus for robust feature matching. IEEE Transactions on Image Processing, 34, 1367-1381.
  28. 28. Zhang, Y., Qiu, C., Liao, X., & Zhang, Y. (2025). Federated hierarchical clustering with automatic selection of optimal cluster numbers. Information Sciences, 122957.
  29. 29. Lyu, M., Torii, R., Liang, C., Peach, T. W., Bhogal, P., Makalanda, L., ... & Chen, D. (2024). Treatment for middle cerebral artery bifurcation aneurysms: in silico comparison of the novel Contour device and conventional flow-diverters. Biomechanics and Modeling in Mechanobiology, 23(4), 1149-1160.
  30. 30. Xing, Z. (2026, May). Design of High-Bandwidth DDR5 MRDIMM Memory Modules for AI Servers. In 2026 IEEE 6th International Conference on Electronic Technology, Communication and Information (ICETCI) (pp. 307-311). IEEE.
  31. 31. Zou, R., Zhang, Y., Zhao, M., Tan, Z., Zhang, Y., & Cheung, Y. M. (2025). SDENK: Unbiased Subspace Density--Clustering. Neurocomputing, 131225.
  32. 32. Wang, T., Deng, X., Sheng, S., & Jia, Y. (2024). Mitigating the damage of a global pandemic on the international buyer-supplier relationship: evidence from Chinese suppliers. Journal of Business Research, 172, 114446.
  33. 33. Cao, Y., Shi, D., Fu, X., Zou, X., Peng, H., Li, X., ... & Xing, J. (2026, March). Multivariate diffusion transformer with decoupled attention for high-fidelity mask-text collaborative facial generation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 4, pp. 2670-2679).
  34. 34. Meng, L., Du, Z., Jia, Z., & Cao, L. (2025). Spatial mechanisms and coupling coordination of cultural heritage and tourism along the Jinzhong segment of the Great Tea Road. Heritage, 9(1), 7.
  35. 35. Tian, Y. (2025). Exploration of power market dynamic scheduling algorithm based on reinforcement learning. In Image Processing, Electronics and Computers (pp. 836-845). IOS Press.
  36. 36. Li, C., Yu, A., Tian, J., Xiang, P., Zhao, Y., Zuo, Y., ... & Xia, L. (2024). Re-entry fiber optic current sensor based on continuous light source. Journal of Lightwave Technology, 43(1), 390-396.
  37. 37. Shi, J., Lin, Y., Hua, Y., Wang, Z., Zhang, Z., Zheng, W., ... & Lu, S. (2026, March). Multiscenario highway lane-change intention prediction: a physics-informed AI framework for three-class classification. In International Conference on Smart Transportation and City Engineering (STCE 2025) (Vol. 14120, pp. 129-145). SPIE.
  38. 38. Chen, Y., Zhong, Y., Wang, Z., Shi, J., & Zheng, W. (2026). Applying the Deep Learning–Sector–Governance (DLSG) Framework to the US Healthcare System: Opportunities, Deployment Pathways, and Policy‑Aligned Evaluation. Journal of Technology Innovation and Society, 4(1), 42-59.
  39. 39. Wang, Y. (2025, April). Efficient adverse event forecasting in clinical trials via transformer-augmented survival analysis. In Proceedings of the 2025 International Symposium on Bioinformatics and Computational Biology (pp. 92-97).
  40. 40. Wang, Y. (2025, June). RAGNet: Transformer–GNN–Enhanced Cox–Logistic Hybrid Model for Rheumatoid Arthritis Risk Prediction. In Proceedings of the 2025 International Conference on Health Informatization and Data Analytics (pp. 90-94).