SEMar 15, 2021

Self-Adaptive Microservice-based Systems -- Landscape and Research Opportunities

arXiv:2103.08688v316 citations
AI Analysis

This work identifies research gaps for improving resilience and performance in microservices, but it is incremental as it reviews existing literature without proposing new methods.

The paper conducted a systematic mapping of 21 studies to analyze the use of self-adaptation techniques in microservice-based systems, finding that most focus on the Monitor phase (28.57%), address self-healing (23.81%), and use reactive strategies (80.95%).

Microservices have become popular in the past few years, attracting the interest of both academia and industry. Despite of its benefits, this new architectural style still poses important challenges, such as resilience, performance and evolution. Self-adaptation techniques have been applied recently as an alternative to solve or mitigate those problems. However, due to the range of quality attributes that affect microservice architectures, many different self-adaptation strategies can be used. Thus, to understand the state-of-the-art of the use of self-adaptation techniques and mechanisms in microservice-based systems, this work conducted a systematic mapping, in which 21 primary studies were analyzed considering qualitative and quantitative research questions. The results show that most studies focus on the Monitor phase (28.57%) of the adaptation control loop, address the self-healing property (23.81%), apply a reactive adaptation strategy (80.95%) in the system infrastructure level (47.62%) and use a centralized approach (38.10%). From those, it was possible to propose some research directions to fill existing gaps.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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