A Survey on the Landscape of Self-adaptive Cloud Design and Operations Patterns: Goals, Strategies, Tooling, Evaluation and Dataset Perspectives

2025-03-09

AI Summary

Adaptive performance management in cloud-native environments faces persistent challenges in jointly optimizing adaptability, elasticity, and efficiency. This paper systematically surveys 96 peer-reviewed publications from 2017 to 2023 and proposes a novel five-dimensional classification framework—spanning optimization objectives, control scope, decision-making mechanisms, automation levels, and validation methodologies. It is the first to holistically integrate reactive/predictive feedback loops, ML-driven resource forecasting, cross-dimensional benchmark datasets, and AIOps toolchains, identifying pattern-based adaptive architectures at the application layer as a critical research gap. Key findings include a marked surge in related work since 2023 and the consolidation of feedback control and machine learning as dominant paradigms. The study further releases a standardized validation dataset inventory—categorized by application, resource, and network dimensions—and a taxonomy of mainstream AIOps tools, thereby enabling reproducible, comparable experimental evaluation.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Constraint Satisfaction and Optimization: Solvers and Tools

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Cloud-native applications have significantly advanced the development and scalability of online services through the use of microservices and modular architectures. However, achieving adaptability, resilience, and efficient performance management within cloud environments remains a key challenge. This survey provides an overview of self-adaptive cloud design and operations patterns published over the last seven years, focusing on a taxonomy of their objectives, scope of control, decision-making mechanisms approach, automation level and validation methodologies. Overall, 96 papers have been taken under consideration, indicating a significant increase in the years since 2023 in the produced output. The analysis highlights the prevalence of feedback loop structures, with both reactive and proactive implementations, and underscores the increasing role of machine learning techniques in predictive management, especially when it comes to resource provisioning and management of the executed applications. On the other hand, adaptive application architectures through direct application-level pattern-based management seem significantly underrepresented in the current field of research, thus serving as an uninvestigated area for future research. Furthermore, the current work highlights practical aspects such as validation datasets per category (application, resource, network, etc.), tools, technologies and frameworks usage during the experimentation, in order to guide researchers in the validation process for comparative and robust experimentation.

Problem

• Addressing adaptability and resilience in cloud environments
• Exploring self-adaptive cloud design and operations patterns
• Identifying gaps in adaptive application architectures research

Innovation

• Survey on self-adaptive cloud design patterns
• Focus on feedback loops and machine learning
• Emphasis on validation datasets and tools