Microservice system solutions are now mainstream. The older microservices-based systems are not more than 15 years old, and their architecture is by far different than the one originally designed because of several changes applied to the systems due to the implementation of new features and bug fixing. The evolution of these legacy systems is therefore subjected to degradation. One of the most important methods to identify degradation is being able to reconstruct the software architecture of a system based on the current system running in production. Different methods have been proposed in the past: methods based on the static analysis of the source code of the microservices and methods based on the analysis of the log traces collected at runtime. Both static and dynamic analysis-based methods have their pros ad cons. In this work, we review the existing technologies for static and dynamic architectural reconstruction and related tools adopted to visualize the reconstructed architecture. The result of this work can be useful both to practitioners and researchers that can further develop these methods to provide better support for architectural degradation. 

Microservice Architecture Reconstruction and Visualization Techniques: A Review

In the landscape of modern software development, the demand for scalability and resilience has become paramount, particularly with the rapid growth of online services and applications. Cloud-native technologies have emerged as a transformative force in addressing these challenges, offering dynamic scalability and robust resilience through innovative architectural approaches. This paper presents a comprehensive review of leveraging cloud-native technologies to enhance scalability and resilience in software development. The review begins by examining the foundational concepts of cloud-native architecture, emphasizing its core principles such as containerization, microservices, and declarative APIs. These principles enable developers to build and deploy applications that can dynamically scale based on demand while maintaining high availability and fault tolerance. Furthermore, the review explores the key components of cloud-native ecosystems, including container orchestration platforms like Kubernetes, which provide automated management and scaling of containerized applications. Additionally, it discusses the role of service meshes in enhancing resilience by facilitating secure and reliable communication between microservices. Moreover, the paper delves into best practices and patterns for designing scalable and resilient cloud-native applications, covering topics such as distributed tracing, circuit breaking, and chaos engineering. These practices empower developers to proactively identify and mitigate potential failure points, thereby improving the overall robustness of their systems. This review underscores the significance of cloud-native technologies in enabling software developers to build scalable and resilient applications. By embracing cloud-native principles and adopting appropriate tools and practices, organizations can effectively meet the evolving demands of modern software development in an increasingly dynamic and competitive landscape.

A comprehensive review of leveraging cloud-native technologies for scalability and resilience in software development

Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey

Fog computing is a distributed paradigm that provides computational resources in the users’ vicinity. Fog orchestration is a set of functionalities that coordinate the dynamic infrastructure and manage the services to guarantee the Service Level Agreements. Monitoring is an orchestration functionality of prime importance. It is the basis for resource management actions, collecting status of resource and service and delivering updated data to the orchestrator. There are several cloud monitoring solutions and tools, but none of them comply with fog characteristics and challenges. Fog monitoring solutions are scarce, and they may not be prepared to compose an orchestration service. This paper updates the knowledge base about fog monitoring, assessing recent subjects in this context like observability, data standardization and instrumentation domains. We propose a novel taxonomy of fog monitoring solutions, supported by a systematic review of the literature. Fog monitoring proposals are analyzed and categorized by this new taxonomy, offering researchers a comprehensive overview. This work also highlights the main challenges and open research questions. 

Monitoring Fog Computing: a Review, Taxonomy and Open Challenges

As microservices become more popular, more drawbacks become apparent to developers. One issue that many teams face today is the failure to visualize the entire system architecture holistically. Without a full view of the system, the architecture can become convoluted as teams add and subtract from their system without reconciling their changes. One established practice to determine a view on the entire system involves dynamic analysis of microservice interaction and dependencies. In this mapping study, we investigate dynamic analysis as a way to visualize system architecture. Capturing the architectural view with dynamic analysis has the ability to build the system and then show its behavior at run-time. We identify dynamic analysis techniques, the corresponding tools, and the models that these practices can generate. The findings of this study are relevant to developers of decentralized systems looking for a way to visualize their system architecture in a dynamic perspective. 

Visualizing Microservice Architecture in the Dynamic Perspective: A Systematic Mapping Study

Microservice-based architectures are gaining popularity for their benefits in software development. Distributed tracing can be used to help operators maintain observability in this highly distributed context, and find problems such as latency, and analyse their context and root cause. However, exploring and working with distributed tracing data is sometimes difficult due to its complexity and application specificity, volume of information and lack of tools. The most common and general tools available for this kind of data, focus on trace-level human-readable data visualisation. Unfortunately, these tools do not provide good ways to abstract, navigate, filter and analyse tracing data. Additionally, they do not automate or aid with trace analysis, relying on administrators to do it themselves. In this paper we propose using tracing data to extract service metrics, dependency graphs and work-flows with the objective of detecting anomalous services and operation patterns. We implemented and published open source prototype tools to process tracing data, conforming to the OpenTracing standard, and developed anomaly detection methods. We validated our tools and methods against real data provided by a major cloud provider. Results show that there is an underused wealth of actionable information that can be extracted from both metric and morphological aspects derived from tracing. In particular, our tools were able to detect anomalous behaviour and situate it both in terms of involved services, work-flows and time-frame. Furthermore, we identified some limitations of the OpenTracing format—as well as the industry accepted tracing abstractions—, and provide suggestions to test trace quality and enhance the standard.

Automated Analysis of Distributed Tracing: Challenges and Research Directions

Microservice architectures have become very popular and widely adopted by the industry, because of the benefits they bring to the software development process and resulting systems, such as parallel development, modularity and scalability. However, as interfaces become more fine-grained and systems grown in size, complexity is moved from the component services to their interactions, eventually leading to intricate workflows that are hard to observe, visualize, and understand. This problem is compounded by the typically high workloads that produce intractable amounts of observation data. To deal with these challenges, operators need support from tools able to take in observation data, in particular tracing, and provide a fast and intuitive understanding of which components or workflows require attention and how are they affecting a module, service, instance, or the whole application. In this paper, we present the design of a microservice visualization application that can fill a gap that exists in leveraging tracing data, aggregating and navigating it in ways that are actionable for operators. Our application provides multiple views of the system and uses spatial and hierarchical navigation using flip zoom to simplify their exploration, while preserving context. Our application can provide a better understanding of the system than existing applications that lack navigability and do not preserve context when switching between different services, layers or views.

μ Viz: Visualization of Microservices

Cloud-native applications consist of highly specialized and decoupled services that can be deployed, scaled and managed independently. Maintaining such applications available is a complex task for operators, because software defects and other kinds of faults can be challenging to diagnose and repair to quickly resume operations. Autonomic service operation is therefore a promising approach. However, there are risks associated to guaranteeing safe autonomic actuation, which must be managed. This paper discusses the challenges identified in the context of the development of a platform for autonomic service operation and describe the software architecture of the platform. Results show mean times to detect, diagnose and repair failures in the order of tens of seconds.

Autonomic Service Operation for Cloud Applications: Safe Actuation and Risk Management

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Automated Analysis of Distributed Tracing: Challenges and Research Directions

μ Viz: Visualization of Microservices

Autonomic Service Operation for Cloud Applications: Safe Actuation and Risk Management