For digital infrastructures closely linked to multi-cloud environments, edge nodes and service meshes, faults may spread across regions, control planes, service calls, data replication, and third-party dependencies. The governance framework based on topology orchestration in this paper consists of dynamic graph modelling, node propagation pressure, edge-level propagation probability, orchestration optimisation and cascading suppression evaluation. Combine the number of cloud service provider regions and statistics on major internet outages to propose a strategy of "topology explicitness—fault domain partitioning—change gating—traffic traction—resilience verification". Therefore, the focus of governance for digital infrastructure should shift from single-point repair to the anticipation of spread conditions. Unify the dependency graph, implement cross-domain scheduling and continuous drills, and thus reduce the cascading effect; improve the continuity and auditability of critical services.

Yiru Zhang. Governance Strategies and Fault Propagation Suppression Methods for Digital Infrastructure Dependent on Topology. International Journal of Multimedia Computing (2026), Vol. 7, Issue 2: 10-16. https://doi.org/10.38007/IJMC.2026.070202

[1] Artime O, Grassia M, De Domenico M, Gleeson J P, Makse H A, Mangioni G, Perc M, Radicchi F. Robustness and resilience of complex networks[J]. Nature Reviews Physics, 2024, 6(2): 114-131.

[2] Alkhateeb A, Jiang S, Charan G. Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond[EB/OL]. arXiv:2301.11283, 2023.

[3] Chang, C. W. (2026). Privacy Infrastructure Vulnerability Mining and Automated Framework Based on Multimodal AI. Procedia Computer Science, 279, 702-711.

[4] Wang, Y. (2026). Construction of Supply Chain Demand Forecasting Algorithm Based On Time Series Data Analysis and Improvement of Its Management Efficiency. Procedia Computer Science, 281, 484-493.

[5] Wang, Z. (2026). Mineral Commodity Time-Series Cycle Modeling and Feature Learning Algorithm Based On Improved Transformer. Procedia Computer Science, 281, 1347-1356.

[6] Liang, Q. (2026). Reconstruction of Commercial Building Space Reuse Mode Driven by Composite Business Types. International Journal of Engineering Advances, 3(1), 107-113.

[7] Gao, Y. (2026). Application of Delaware Corporate Law in Cross-Border M&A: Business Structures, Contractual Risk, and Case-Based Lessons. Economics and Management Innovation, 3(2), 128-136.

[8] Yin, J. (2025). Research on Financial Time Series Prediction Model Based on Multi Attention Mechanism and Emotional Feature Fusion. Socio-Economic Statistics Research (2025), 6(2), 161-169.

[9] Liang, Q. (2026). Research on the Renovation Design Path for Enhancing the Efficiency of Mixed-Use Office and Retail Spaces. European Journal of AI, Computing & Informatics, 2(2), 163-170.

[10] Liang, Q. (2026). How Architectural Design and Utility Infrastructure Impacts AI Supporting Campus and Drive Future Innovation, Operational Efficiency and Sustainable Advancement in Utility-Critical Environment. European Journal of Engineering and Technologies, 2(2), 71-77.

[11] Liu, Z. (2026). Research on Measuring User Behavior Response Differences Supported by Propensity Scoring Method. European Journal of AI, Computing & Informatics, 2(2), 47-53.

[12] Gao, Y. (2026). Governance Structures and Checks and Balances in Private Equity Funds: Practice, Problems, and Improvement Paths. Financial Economics Insights, 3(2), 82-91.

[13] Liang, Q. (2026). Research on Low-Energy Space Organization Methods in the Context of Building System Integration. Global Journal of Science & Innovation, 3(1), 9-15.