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| Title | Sdt-Mcs: Topology-Aware Microservice Orchestration With Adaptive Learning In Cloud-Edge Environments |
|---|---|
| ID_Doc | 47495 |
| Authors | Zhu J.; Chen H.; Wang H. |
| Year | 2025 |
| Published | Concurrency and Computation: Practice and Experience, 37, 18-20 |
| DOI | http://dx.doi.org/10.1002/cpe.70176 |
| Abstract | The exponential growth of IoT devices poses unprecedented challenges to cloud-edge collaboration, particularly in microservice-based industrial scenarios where real-time sensor data flows through complex service chains. Current approaches suffer from critical issues: Inefficient service deployment, load imbalance, and service instability. Specifically, the performance degradation is particularly severe when critical path services experience sudden load spikes, often leading to cascading delays across entire service chains. This paper presents SDT-MCS (Service Dependency Topology-aware Microservice Collaborative Scheduling), a framework that combines topology-oriented federated learning with lightweight Actor-Critic reinforcement mechanisms. For resource collaboration, we propose a topology-aware pre-deployment algorithm that leverages federated learning to optimize global resource orchestration while considering both resource constraints and service dependencies. For service collaboration, we design a chain-based scheduling mechanism that employs Actor-Critic reinforcement learning for local dynamic adjustment, enabling rapid response to workload variations while maintaining service chain stability. We implement our framework on EdgeCloudSim and evaluate it using production workload traces from industrial robotics and smart city scenarios, with additional validation on a physical testbed. Experimental results demonstrate that our topology-aware pre-deployment reduces average latency by 31.6% in ETL scenarios compared to baseline approaches. Furthermore, our chain-based scheduling achieves 35.4% latency reduction under high concurrency while maintaining service stability between 65%–70% through dynamic load balancing. © 2025 John Wiley & Sons Ltd. |
| Author Keywords | cloud-edge collaboration; dynamic resource allocation; service chain optimization; topology-aware scheduling |
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