Modeling Big Data Systems by Extending the Palladio Component Model

ABSTRACT The growing availability of big data has induced new storing and processing techniques implemented in big data systems such as Apache Hadoop or Apache Spark. With increased implementations of these systems in organizations, simultaneously, the requirements regarding performance qualities such as response time, throughput, and resource utilization increase to create added value. Guaranteeing these performance requirements as well as efficiently planning needed capacities in advance is an enormous challenge. Performance models such as the Palladio component model (PCM) allow for addressing such problems. Therefore, we propose a metamodel extension for PCM to be able to model typical characteristics of big data systems. The extension consists of two parts. First, the meta-model is extended to support parallel computing by forking an operation multiple times on a computer cluster as intended by the single instruction, multiple data (SIMD) architecture. Second, modeling of computer clusters is integrated into the meta-model so operations can be properly scheduled on contained computing nodes

Context-Based Confidentiality Analysis for Industrial IoT

This dataset contains the models and source code regarding the work described in the paper "Context-Based Confidentiality Analysis for Industrial IoT". To use the content of this dataset, a working setup of the source code and models of the dataset https://zenodo.org/record/2574147#.XoC5KogzZPY, is needed as a base.This dataset is still actively worked on. An updated version might be published in the future. This work was supported by the German Federal Ministry of Education and Research under grant number 01IS17106A (Trust 4.0)

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Efficiently Conducting Quality-of-Service Analyses by Templating Architectural Knowledge

Previously, software architects were unable to effectively and efficiently apply reusable knowledge (e.g., architectural styles and patterns) to architectural analyses. This work tackles this problem with a novel method to create and apply templates for reusable knowledge. These templates capture reusable knowledge formally and can efficiently be integrated in architectural analyses

A generic formalism for the semantic modeling and representation of architectural elements

This article presents a methodological approach to the semantic description of architectural elements based both on theoretical reflections and research experiences. To develop this approach, a first process of extraction and formalization of architectural knowledge on the basis of the analysis of architectural treaties is proposed. Then, the identified features are used to produce a template shape library dedicated to buildings surveying. Finally, the problem of the overall model structuring and organization using semantic information is addressed for user handling purposes