Abstract

DevOps and Continuous Delivery (CD) are becoming the de-facto standard for software deployment in the cloud. Deployment pipelines are a core artefact in such practices, but so far their design is largely discussed informally, and existing sources on DevOps and CD practices are often inconsistent or incomplete. The lack of a generic, complete, tool-agnostic, and application-independent treatment of deployment pipeline design and architecture impedes their understanding by human designers and the creation of generic tools that work across different technologies and applications. To alleviate this problem, we have performed a qualitative, in-depth study of 25 deployment practice descriptions by practitioners containing informal deployment pipeline models. From our study we derived a precisely specified model of deployment pipeline architectures. We can show that the formal model substantially increases the precision of the modelling compared to the informally modelled pipelines in the original sources. Document type: Conference object

Abstract

One approach to continuously achieve a certain data quality level is to use an integration pipeline that continuously checks and monitors the quality of a data set according to defined metrics. This approach is inspired by Continuous Integration pipelines, that have been introduced in the area of software development and DevOps to perform continuous source code checks. By investigating in possible tools to use and discussing the specific requirements for RDF data sets, an integration pipeline is derived that joins current approaches of the areas of software-development and semantic-web as well as reuses existing tools. As these tools have not been built explicitly for CI usage, we evaluate their usability and propose possible workarounds and improvements. Furthermore, a real-world usage scenario is discussed, outlining the benefit of the usage of such a pipeline.

Abstract

Container-based application deployments have received significant attention in recent years. Operating system virtualization based on containers as a mechanism to deploy and manage complex, large-scale software systems has become a popular mechanism for application deployment and operation. Packaging application components into selfcontained artifacts has brought substantial flexibility to developers and operation teams alike. However, this flexibility comes at a price. Practitioners need to respect numerous constraints ranging from security and compliance requirements, to specific regulatory conditions. Fulfilling these requirements is especially challenging in specialized domains with large numbers of stakeholders.Moreover, the rapidly growing number of container images to be managed due to the introduction of new or updated applications and respective components, leads to significant challenges for container management and adaptation. In this paper, we introduce Smart Brix, a framework for continuous evolution of container application deployments that tackles these challenges. Smart Brix integrates andunifies concepts of continuous integration, runtimemonitoring, and operational analytics. Furthermore, it allows practitioners to define generic analytics and compensation pipelines composed of self-assembling processing components to autonomously validate and verify containers to be deployed.We illustrate the feasibility of our approach by evaluating our framework using a case study from the smart city domain. We show that Smart Brix is horizontally scalable and runtime of the implemented analysis and compensation pipelines scales linearly with the number of container application packages. Subjects Adaptive and Self-Organizing Systems, Distributed and Parallel Computing, Software Engineering Document type: Article