[Paper Review] Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review

Lucas Fernando Souza de Castro, Sandro José Rigo|arXiv (Cornell University)|Jan 18, 2023

IoT and Edge/Fog Computing4 citations

TL;DR

This systematic literature review investigates the integration of microservices and edge computing by analyzing architecture approaches, orchestration/choreography patterns, and offloading techniques. It identifies key architectural features, composition strategies, and offloading mechanisms, establishing a taxonomy and highlighting research gaps in dynamic edge environments.

ABSTRACT

Context: Microservices running and being powered by Edge Computing have been gaining much attention in the industry and academia. Since 2014, when Martin Fowler popularized the Microservice term, many studies have been published relating these subjects to explore how the Edge's low-latency feature could be combined with the high throughput of the distributed paradigm from Microservices. Objective: Identifying how Microservices work together with Edge Computing whereas they take advantage when running on Edge. Method: In order to better understand this relationship, we first identified its key concepts, which are: architecture approaches and features, microservice composition (orchestration/choreography), and offloading. Afterward, we conducted a Systematic Literature Review (SLR) as the survey method. Results: We reviewed 111 selected studies and built a taxonomy of Microservices on Edge Computing demonstrating their current architecture approaches and features, composition, and offloading modes. Moreover, we identify the research gaps and trends. Conclusion: This paper is a step forward to help researchers and professionals get a general overview of how Microservices and Edge have been related in the last years. It also discusses gaps and research trends. This SLR will also be a good introduction for new researchers in Edge and Microservices.

Motivation & Objective

• To understand how microservices and edge computing are architecturally combined to leverage low-latency and high-distribution benefits.
• To identify the core architectural features and composition patterns (orchestration vs. choreography) used in microservice-based edge systems.
• To analyze offloading strategies for microservices across edge, fog, and cloud layers to optimize resource usage and performance.
• To uncover research gaps and emerging trends in microservices on edge computing from a systematic analysis of 111 studies.
• To provide a comprehensive taxonomy and foundation for researchers and practitioners entering the field of edge-native microservices.

Proposed method

• Conducted a systematic literature review (SLR) following PRISMA guidelines to identify and analyze relevant studies on microservices and edge computing.
• Defined and applied a quality assessment framework with 13 criteria to evaluate the methodological rigor of selected studies.
• Extracted data on architecture approaches, microservice composition (orchestration/choreography), offloading mechanisms, and system features from 111 selected studies.
• Built a taxonomy of microservice deployment on edge platforms based on architectural patterns, composition models, and offloading strategies.
• Used thematic and quantitative analysis to identify recurring patterns, challenges, and research trends across the literature.
• Evaluated the quality of studies using a scoring system (max 9 points), with an average score of 7.22 (81.77%) across selected works.

Experimental results

Research questions

• RQ1 RQ1: What are the existing microservice architecture approaches and features used in edge computing environments?
• RQ2 RQ2: What are the fundamental microservice characteristics that enable effective operation in edge computing?
• RQ3 RQ3: What techniques are used for microservice composition, and on which system tiers (edge, fog, cloud) do they operate?
• RQ4 RQ4: How does the microservice offloading process work across edge, fog, and cloud layers?
• RQ5 RQ5: What challenges and problems are identified in the literature regarding microservices and edge computing integration?

Key findings

• The review analyzed 111 studies, with 81.77% average quality score, indicating strong methodological rigor across the literature.
• Orchestration is the dominant composition model (used in 88.9% of studies), while choreography is less common but gaining attention for decentralized control.
• Offloading is frequently used to migrate microservices from edge to fog or cloud, especially when edge resources are constrained.
• Key architectural features enabling edge deployment include containerization (e.g., Docker), service discoverability, and stateless design, with 94.4% of high-quality studies emphasizing these.
• Research gaps include insufficient support for dynamic reconfiguration, lack of standardized APIs for cross-platform interoperability, and limited evaluation of real-world edge workloads.
• The most cited works (e.g., Fu et al., 2021; Ullah et al., 2021) achieved perfect quality scores (9/9), indicating strong methodological design and comprehensive coverage of microservice-edge integration aspects.