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This survey analyzes adaptive management techniques for microservice-based cloud applications, focusing on how they address dynamic workloads, network conditions, and failures. The paper presents a taxonomy based on control locus, modeled dynamics, adaptation strategy, and evaluation evidence, synthesizing findings from 84 systems and 13 evaluation artifacts. The analysis reveals that current systems often only partially model production dynamics, and reported gains are sensitive to evaluation fidelity, highlighting the need for more comprehensive and reproducible evaluations.
Current adaptive microservice management systems only scratch the surface of real-world production dynamics, and their purported gains may be overstated.
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.
