design microservices architecture

Microservices achieve physical isolation but fail to prevent the proliferation of logical coupling, undermining module independence. This paper proposes a novel modularization paradigm based on universal interface boundaries, constructs a quantifiable model for assessing module independence, and designs a runtime mechanism supporting dynamic loading, unloading, and hot updates within a single process. Its core contributions are: (1) reframing module independence as a formal, modelable, and measurable system property—moving beyond qualitative assertions; (2) replacing implicit dependencies with explicit interface contracts to fundamentally block coupling propagation; and (3) implementing the EIGHT platform prototype, which achieves microservice-level module autonomy within a monolithic process. Experimental results demonstrate that the approach significantly reduces the impact scope of cross-module changes, enhancing system maintainability and evolutionary efficiency. It provides both theoretical foundations and practical pathways for next-generation architectures transcending the monolith–microservice dichotomy.

This study addresses the lack of systematic guidance for enterprise software teams in choosing between monolithic and microservices architectures. The work proposes a decision-making framework that integrates technical and organizational factors, evaluating the trade-offs of each architecture across dimensions such as scalability, reliability, deployment efficiency, and organizational complexity. The assessment is grounded in system scale, business requirements, operational maturity, and long-term maintainability. Through architectural pattern analysis, a structured evaluation model, and multiple case studies, the authors develop a practical selection methodology tailored to real-world engineering contexts. This approach offers enterprises clear architectural evolution pathways and actionable guidelines aligned with their developmental stages, thereby significantly enhancing the rationality and sustainability of system design decisions.

Distributed heterogeneous modular space robotic systems face significant challenges in architectural design and dynamic deployment across diverse computing platforms, network conditions, and planetary environments. Method: This paper proposes a distributed heterogeneous modular design paradigm integrating software components, communication infrastructure, and orchestration layers. Leveraging component-based principles, it establishes a reusable architecture supporting dynamic reconfiguration, decentralized control, and multi-module coordination. The approach integrates ROS 2 with the Zenoh data-centric middleware for low-latency, high-reliability communication, incorporates an open-source motion stack, and introduces a lightweight deployment orchestrator. Contribution/Results: Extensive field validation demonstrates robust support for self-assembling robots, multi-robot collaboration, and teleoperation. The system substantially reduces integration and maintenance overhead while enabling universal deployment across heterogeneous hardware platforms, development teams, and mission-specific operational environments.

Contract-based design (CbD) lacks systematic empirical evidence supporting its application in trustworthy software systems. Method: This study conducts the first systematic mapping study (SMS) of CbD across the full lifecycle and multiple dimensions for trustworthy systems, employing tri-database collaborative retrieval, collaborative review, and voting-based screening to analyze 288 primary studies via thematic coding and evidence aggregation. Contribution/Results: The study clarifies the breadth and depth of CbD adoption across domains, quantitatively assesses domain maturity distributions, and identifies critical gaps among theoretical modeling, automated verification, and industrial practice. It proposes six empirically grounded, verifiable research directions and delivers the first evidence-based CbD methodology framework and practical guidelines for trustworthy software engineering.

This work addresses the challenges posed by microservice architectures—such as missing documentation, difficulty in understanding system structure, and architectural drift—stemming from decentralized, multi-repository development practices. Existing refactoring approaches struggle to generalize across projects due to their lack of modularity and technology-stack dependency. To overcome these limitations, we propose ModARO, a modular and technology-agnostic architecture reconstruction method that leverages reusable “extractors” to automatically analyze distributed codebases and reconstruct system architectures across projects. ModARO seamlessly integrates into CI/CD pipelines, enabling continuous architectural awareness. Empirical evaluation demonstrates its successful application to ten open-source systems, and a user study with eight industry practitioners confirms its significant superiority over existing baselines in terms of practicality and usability.

This work addresses the persistent challenge of inconsistent development and execution environments faced by researchers operating across heterogeneous computing platforms—ranging from laptops and workstations to supercomputers and cloud infrastructures. To overcome this, the authors propose a modular and portable software ecosystem featuring a unified command-line interface that enables seamless orchestration and execution of scientific workflows. The system ensures cross-platform consistency, reproducibility, and scalability, thereby streamlining computational research across diverse hardware configurations. Its practical efficacy has been demonstrated through successful integration into the plan4res project under the European Union’s Horizon 2020 initiative, where it effectively supported complex, large-scale scientific workflows in varied computing environments.

This work addresses the challenge of effectively evaluating the trade-offs between data consistency and coordination overhead among distributed transaction patterns—such as Saga and TCC—in business logic-intensive microservice systems prior to production deployment. The authors propose a lightweight microservice simulator grounded in Domain-Driven Design (DDD), which, for the first time, integrates DDD aggregate root modeling with multiple transaction models to decouple business logic from communication and transactional infrastructure. The framework supports configurable deployment topologies and network constraints, enabling seamless transitions from centralized to fully distributed architectures while providing a deterministic verification environment. Empirical evaluation on complex multi-aggregate systems quantifies the performance, coordination overhead, and resilience of different transaction models, substantially reducing development costs and facilitating left-shifted architectural validation.

This work addresses the lack of unified and reproducible evaluation criteria for service boundary identification in the migration from monolithic systems to microservices. It presents the first systematic comparison of mainstream microservice decomposition approaches—static, dynamic, and hybrid—within a consistent experimental framework. Using standardized metric computation procedures and multiple benchmark systems (JPetStore, AcmeAir, DayTrader, Plants), the study evaluates key dimensions including structural modularity (SM), interface count (IFN), inter-component communication (ICP), and non-extreme distribution (NED). Experimental results demonstrate that HDBScan with hierarchical clustering consistently yields highly cohesive and loosely coupled microservice partitions across benchmarks, achieving an optimal trade-off between modularity strength and communication overhead, thereby significantly enhancing the objectivity and reproducibility of microservice decomposition evaluation.

This study addresses the limitations of traditional Design Structure Matrix (DSM) modularization approaches, which rely solely on graph-based optimization and lack engineering semantic context, often failing to align with practical design requirements. The authors propose a novel DSM modularization paradigm integrating large language models (LLMs), leveraging prompt engineering and iterative refinement to embed system-level semantic information directly into the partitioning process—achieving high-quality results without custom optimization code. Central to this work is the "semantic alignment hypothesis," which elucidates how improper incorporation of domain knowledge can degrade performance. Through systematic experiments across five representative engineering cases using three mainstream LLMs, the method demonstrates convergence to reference-quality modularization within 30 iterations, offering a reproducible and practical pathway for LLM-driven engineering design optimization.

This work addresses the challenge of ensuring safety, reliability, and trustworthiness in collective adaptive systems operating in dynamic environments by proposing a modular design paradigm centered on intrinsic trustworthiness. The approach integrates a runtime model based on local causal event sequences, a temporal logic verification technique supporting modular architectures, and a compositional reasoning mechanism for global system properties grounded in component attributes. Through this tripartite framework, the study overcomes key limitations of conventional formal methods and demonstrates substantial improvements in verifiability and scalability in case studies, thereby establishing both a theoretical foundation and a practical pathway for engineering highly trustworthy collective adaptive systems.