Adaptive Management of Microservices in Dynamic Computing Environments: A Taxonomy and Future Directions
For researchers and practitioners in cloud computing, this survey provides a structured taxonomy and identifies gaps in current adaptive management approaches for microservices.
This survey examines dynamics-aware adaptive management for microservices, proposing a taxonomy covering control locus, modeled dynamics, adaptation strategy, and evaluation evidence. A synthesis of 84 systems shows that production dynamics are often partially modeled and that reported gains depend on evaluation fidelity.
Microservice-based cloud applications face changing workloads, evolving request paths, variable network conditions, interference, and failures. These dynamics couple autoscaling, placement, routing, isolation, and remediation. The survey examines dynamics-aware adaptive management for microservices. Its taxonomy covers control locus, modeled dynamics, adaptation strategy, and evaluation evidence; objectives and telemetry are cross-cutting. A synthesis of 84 system entries and 13 evaluation artifacts shows that production dynamics are often partially modeled. Reported gains also depend on evaluation fidelity. Key future directions include cross-layer coordination, telemetry-to-control abstractions, safe learning-based control, and reproducible dynamic evaluation.