Modern digital services are typically composed of multiple interconnected components,each dedicated to a specific functionality. These components can be dynamically instanti-ated, terminated, and scaled on demand across the available network nodes. Data trafficis intelligently directed to interconnect these components, thereby forming diverse end-to-end (E2E) service chains. While such service function chaining enables flexible andefficient service deployment, it also presents a range of emerging challenges. These in-clude determining the optimal placement of service functions, efficiently routing traffic toavoid new bottlenecks—particularly in scenarios where traditional IP traffic coexists—andselecting alternative components or paths in the event of failures. This study addressesseveral of these challenges by proposing innovative solutions across a variety of networkscenarios.ˆ A Centralised Solution for SFC in Hybrid Networks: Unlike existing state-of-the-art studies that typically handle Service Function Chaining (SFC) flows in-dividually, this study proposes a centralised solution that jointly considers all SFCflows along with background traffic. By addressing resource competition in a holis-tic manner, the proposed approach reduces Maximum Link Utilisation (MLU) andVirtual Network Function (VNF) Maximum Utilisation (VMU) by 30–40% com-pared with traditional shortest path algorithms. Compared with optimal solutionsobtained through Mixed Integer Linear Programming, this method reduces compu-tation time from several days to tens of seconds while achieving performance thatclosely approximates the optimal results.ˆ A Unified Model for Diverse Network Failures: This study introduces anovel unified model that integrates Graph Neural Network with Deep ReinforcementLearning to enhance resilience against unpredictable link and VNF node failures.The proposed algorithm is capable of rapidly selecting alternative VNF nodes androuting paths while maintaining performance levels comparable to those achieved bythe centralized solution prior to failures. This unified model eliminates the need totrain tens or hundreds of separate models for different failure scenarios; instead, asingle trained model can generalize across all unpredictable failure cases.ˆ A Cluster-Based Distributed Approach for Balancing Performance andPrivacy: Existing solutions are typically either fully distributed or centralized, mak-ing them inflexible for deployment across different scenarios. To address this limi-tation, this study proposes an innovative cluster-based framework with user-definedclustering schemes, introduces information-sharing mechanisms among clusters, al-lowing it to be adapted to fully distributed, centralized, or hybrid cluster-baseddeployments. The clustering approach provides a balanced trade-off between net-work information privacy and overall system performance. Compared with SOTAbenchmarks, this approach achieves approximately 20% better performance in thesame scenario.