Adaptive Management of Microservices in Dynamic Computing Environments: A Taxonomy and Future Directions

TL;DR

This survey analyzes how microservices adapt to dynamic cloud environments, proposing a taxonomy and highlighting future research directions for more effective management.

Contribution

It provides a comprehensive taxonomy of dynamics-aware microservice management and synthesizes existing systems and evaluation methods, identifying gaps and future challenges.

Findings

Production dynamics are often only partially modeled.

Evaluation gains depend heavily on fidelity.

Future directions include cross-layer coordination and safe learning-based control.

Abstract

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.