1,721,422 research outputs found
Toward bundler-independent module federations: enabling typed micro-frontend architectures
No full textModern web applications demand scalable and modular architectures, driving the adoption of micro-frontends. This paper introduces Bundler-Independent Module Federation (BIMF) as a New Idea, enabling runtime module loading without relying on traditional bundlers, thereby enhancing flexibility and team collaboration. This paper presents the initial implementation of BIMF, emphasizing benefits such as shared dependency management and modular performance optimization. We address key challenges, including debugging, observability, and performance bottlenecks, and propose solutions such as distributed tracing, server-side rendering, and intelligent prefetching. Future work will focus on evaluating observability tools, improving developer experience, and implementing performance optimizations to fully realize BIMF's potential in micro-frontend architectures.</p
Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
No full textContext: The combination of distributed stream processing with microservice architectures is an emerging pattern for building data-intensive software systems. In such systems, stream processing frameworks such as Apache Flink, Apache Kafka Streams, Apache Samza, Hazelcast Jet, or the Apache Beam SDK are used inside microservices to continuously process massive amounts of data in a distributed fashion. While all of these frameworks promote scalability as a core feature, there is only little empirical research evaluating and comparing their scalability. Objective: The goal of this study to obtain evidence about the scalability of state-of-the-art stream processing framework in different execution environments and regarding different scalability dimensions. Method: We benchmark five modern stream processing frameworks regarding their scalability using a systematic method. We conduct over 740 h of experiments on Kubernetes clusters in the Google cloud and in a private cloud, where we deploy up to 110 simultaneously running microservice instances, which process up to one million messages per second. Results: All benchmarked frameworks exhibit approximately linear scalability as long as sufficient cloud resources are provisioned. However, the frameworks show considerable differences in the rate at which resources have to be added to cope with increasing load. There is no clear superior framework, but the ranking of the frameworks depends on the use case. Using Apache Beam as an abstraction layer still comes at the cost of significantly higher resource requirements regardless of the use case. We observe our results regardless of scaling load on a microservice, scaling the computational work performed inside the microservice, and the selected cloud environment. Moreover, vertical scaling can be a complementary measure to achieve scalability of stream processing frameworks. Conclusion: While scalable microservices can be designed with all evaluated frameworks, the choice of a framework and its deployment has a considerable impact on the cost of operating it.</p
Drivers and barriers for microservice adoption – a survey among professionals in Germany
No full textMicroservices are an architectural style for software which currently receives a lot of attention in both industry and academia. Several companies employ microservice architectures with great success, and there is a wealth of blog posts praising their advantages. Especially so-called Internet-scale systems use them to satisfy their enormous scalability requirements and to rapidly deliver new features to their users. But microservices are not only popular with large, Internet-scale systems. Many traditional companies are also considering whether microservices are a viable option for their applications. However, these companies may have other motivations to employ microservices, and see other barriers which could prevent them from adopting microservices. Furthermore, these drivers and barriers possibly differ among industry sectors. In this article, we present the results of a survey on drivers and barriers for microservice adoption among professionals in Germany. In addition to overall drivers and barriers, we particularly focus on the use of microservices to modernize existing software, with special emphasis on implications for runtime performance and transactionality. We observe interesting differences between early adopters who emphasize scalability of their Internet-scale systems, compared to traditional companies which emphasize maintainability of their IT systems.</p
The Titan Control Center for Industrial DevOps analytics research
No full textThe Titan Control Center is a software platform supporting research on industrial big data analytics. Building upon a scalable and extensible architecture, the Titan Control Center analyzes and visualizes data streams from Internet of Things sensors in industrial production. It performs different types of aggregations, correlation, forecasting, and anomaly detection to provide deeper insights into industrial production data for enabling Industrial DevOps. Furthermore, the Titan Control Center is used in research for implementing and evaluating novel approaches on multi-dimensional sensor data stream aggregation, as a reference platform for benchmarking scalability in modular analytics software, and for research on analyzing industrial energy consumption.</p
Microservice architectures for scalability, agility and reliability in e-commerce
No full textMicroservice architectures provide small services that may be deployed and scaled independently of each other, and may employ different middleware stacks for their implementation. Microservice architectures intend to overcome the shortcomings of monolithic architectures where all of the application's logic and data are managed in one deployable unit. We present how the properties of microservice architectures facilitate scalability, agility and reliability at otto.de, which is one of the biggest European e-commerce platforms. In particular, we discuss vertical decomposition into self contained systems and appropriate granularity of microservices as well as coupling, integration, scalability and monitoring of microservices at otto.de. While increasing agility to more than 500 live deployments per week, high reliability is achieved by means of automated quality assurance with continuous integration and deployment.</p
Demo Paper: Benchmarking Scalability of Cloud-Native Applications with Theodolite
No full textTheodolite is a framework for benchmarking the scalability of cloud-native applications. It automates deployment and monitoring of a cloud-native application for different load intensities and provisioned cloud resources and assesses whether specified service level objectives (SLOs) are fulfilled. Provided as a Kubernetes Operator, Theodolite allows defining, sharing, and archiving benchmarks and experiment configurations in declarative files. We demonstrate Theodolite’s benchmarking method and show how researchers and cloud engineers can execute existing scalability benchmarks or define new ones with Theodolite
Fast and efficient what-if analyses of invocation overhead and transactional boundaries to support the migration to microservices
No full textImproving agility and maintainability are common drivers for companies to adopt a microservice architecture for their existing software systems. However, the existing software often relies heavily on the fact that it is executed within a single process space. Therefore, decomposing existing software into out-of-process components like microservices can have a severe impact on non-functional properties, such as overall performance due to invocation overhead or data consistency. To minimize this impact, it is important to consider non-functional properties already as part of the design process of the service boundaries. A useful method for such considerations are what-if analyses, which allow to explore different scenarios and to develop the service boundaries in an iterative and incremental way. Experience from an industrial case study suggests that for these analyses, ease of use and speed tend to be more important than precision. In this paper, we present emerging results for an approach for what-if analyses based on trace rewriting that is (i) specifically designed for analyzing the impact on non-functional properties due to decomposition into out-of-process components and (ii) deliberately prefers ease of use and analysis speed over precision of the results.</p
Continuous API Evolution in Heterogenous Enterprise Software Systems
No full textThe ability to independently deploy parts of a software system is one of the cornerstones of modern software development, and allows for these parts to evolve independently and at different speeds.A major challenge of such independent deployment, however, is to ensure that despite their individual evolution, the interfaces between interacting parts remain compatible. This is especially important for enterprise software systems, which are often highly integrated and based on heterogenous IT infrastructures. Although several approaches for interface evolution have been proposed, many of these rely on the developer to adhere to certain rules, but provide little guidance for doing so. In this paper, we present an approach for interface evolution that is easy to use for developers, and also addresses typical challenges of heterogenous enterprise software, especially legacy system integration.</p
From digital twins to digital twin prototypes: concepts, formalization, and applications
No full textThe transformation to Industry 4.0 also transforms the processes of developing intelligent manufacturing production systems. Digital twins may be employed to advance the development of these new (embedded) software systems. However, there is no consensual definition of what a digital twin is. In this paper, we provide an overview of the current state of the digital twin concept and formalize the digital twin concept using the Object-Z notation. This formalization includes the concepts of physical twins, digital models, digital templates, digital threads, digital shadows, digital twins, and digital twin prototypes. The relationships between all these concepts are visualized as class diagrams using the Unified Modeling Language. Our digital twin prototype approach supports engineers in the development and automated testing of complex embedded software systems. This approach enables engineers to test embedded software systems in a virtual context without the need of a connection to a physical object. In continuous integration/continuous deployment pipelines, such digital twin prototypes can be used for automated integration testing and, thus, allow for an agile verification and validation process. In this paper, we demonstrate and report on the application and implementation of a digital twin using the example of two real-world field studies (ocean observation systems and smart farming). For independent replication and extension of our approach by other researchers, we provide a laboratory study published open source on GitHub.</p
A configurable method for benchmarking scalability of cloud-native applications
No full textCloud-native applications constitute a recent trend for designing large-scale software systems. However, even though several cloud-native tools and patterns have emerged to support scalability, there is no commonly accepted method to empirically benchmark their scalability. In this study, we present a benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, frameworks, and deployment options. Our benchmarking method consists of scalability metrics, measurement methods, and an architecture for a scalability benchmarking tool, particularly suited for cloud-native applications. Following fundamental scalability definitions and established benchmarking best practices, we propose to quantify scalability by performing isolated experiments for different load and resource combinations, which asses whether specified service level objectives (SLOs) are achieved. To balance usability and reproducibility, our benchmarking method provides configuration options, controlling the trade-off between overall execution time and statistical grounding. We perform an extensive experimental evaluation of our method’s configuration options for the special case of event-driven microservices. For this purpose, we use benchmark implementations of the two stream processing frameworks Kafka Streams and Flink and run our experiments in two public clouds and one private cloud. We find that, independent of the cloud platform, it only takes a few repetitions (≤ 5) and short execution times (≤ 5 minutes) to assess whether SLOs are achieved. Combined with our findings from evaluating different search strategies, we conclude that our method allows to benchmark scalability in reasonable time.</p
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