[2007] Modellbasierte Analyse interorganisationaler Wissensflüsse (Nane Kratzke), 2007. [bib]
Artikel and Buchkapitel
[2018] About the Complexity to Transfer Cloud Applications at Runtime and how Container Platforms can Contribute? (Nane Kratzke), Chapter in Cloud Computing and Services Science (revised selected papers) Springer (Markus Helfert, Donald Ferguson, Victor Mendez Munoz, Jorge Cardoso, eds.), 2018. [bib]
[2017] The #BTW17 Twitter Dataset - Recorded Tweets of the Federal Election Campaigns of 2017 for the 19th German Bundestag (Nane Kratzke), In Data, volume 2, 2017. [bib] [pdf]
[2017] Understanding Cloud-native Applications after 10 Years of Cloud Computing - A Systematic Mapping Study (Nane Kratzke, Peter-Christian Quint), In Journal of Systems and Software Elsevier, volume 126, 2017. [bib] [abstract]
It is common sense that cloud-native applications (CNA) are intentionally designed for the cloud. Although this understanding can be broadly used it does not guide and explain what a cloud-native application exactly is. The term "cloud-native" was used quite frequently in birthday times of cloud computing (2006) which seems somehow obvious nowadays. But the term disappeared almost completely. Suddenly and in the last years the term is used again more and more frequently and shows increasing momentum. This paper summarizes the outcomes of a systematic mapping study analyzing research papers covering "cloud-native" topics, research questions and engineering methodologies. We summarize research focuses and trends dealing with cloud-native application engineering approaches. Furthermore, we provide a definition for the term "cloud-native application" which takes all findings, insights of analyzed publications and already existing and well-defined terminology into account.
[2017] Investigation of Impacts on Network Performance in the Advance of a Microservice Design (Nane Kratzke, Peter-Christian Quint), Chapter in Cloud Computing and Services Science (revised selected papers) Springer (Markus Helfert, Donald Ferguson, Victor Mendez Munoz, Jorge Cardoso, eds.), 2017. [bib]
[2016] Vendor Lock-In im Cloud Computing! Was bringen Container und Container-Cluster? (Peter-Christian Quint), In OBJEKTspektrum, Ausgabe Online Themenspecial Microservices und Docker 2016, 2016. [bib]
[2016] Taming the Complexity of Elasticity, Scalability and Transferability in Cloud Computing - Cloud-Native Applications for SMEs (Peter-Christian Quint, Nane Kratzke), In International Journal on Advances in Networks and Services International Academy, Research, and Industry Association (IARIA), volume 9, 2016. [bib] [abstract]
Cloud computing enables companies getting computational and storage resources on demand. Especially when using features like elasticity and scaling, cloud computing can be a very powerful technology to run, e.g., a webservice without worries about failure by overload or wasting money by paid use of unneeded resources. For using these features, developers can use or implement cloud-native applications (CNA), containerized software running on an elastic platform. Nevertheless, a CNA can be complex at planning, installation and configuration, maintenance and searching for failures. Small and medium enterprises (SMEs) are mostly limited by their personnel and financial restrictions. So, using these offered services can facilitate a very fast realization of the software project. However, using these (proprietary) services it is often difficult to migrate between cloud vendors. This paper introduces C4S, an open source system for SMEs to deploy and operate their container application with features like elasticity, auto-scaling and load balancing. The system also supports transferability features for migrating containers between different Infrastructure as a Service (IaaS) platforms. Thus, C4S is a solution for SMEs to use the benefits of cloud computing with IaaS migration features to reduce vendor lock-in.
[2016] Project Cloud TRANSIT - Or to Simplify Cloud-native Application Provisioning for SMEs by Integrating Already Available Container Technologies (Nane Kratzke, Peter-Christian Quint, Derek Palme, Dirk Reimers), Chapter in European Project Space on Smart Systems, Big Data, Future Internet - Towards Serving the Grand Societal Challenges Scitepress (Verena Kantere, Barbara Koch, eds.), 2016. [bib]
[2016] Public Cloud Services an der Fachhochschule Lübeck: Betrachtungen zu wirtschaftlich sinnvollen Einsatzgebieten in Lehre und Forschung (Nane Kratzke, Andreas Hanemann), In Impulse - Aus Forschung und Lehre der FH Lübeck, volume 19, 2016. [bib]
[2015] Sperrvermerke bei Abschlussarbeiten (Nane Kratzke), In Informatik-Spektrum Springer Berlin Heidelberg, 2015. [bib] [pdf]
[2015] How to Operate Container Clusters more Efficiently? Some Insights Concerning Containers, Software-Defined-Networks, and their sometimes Counterintuitive Impact on Network Performance (Nane Kratzke, Peter-Christian Quint), In International Journal On Advances in Networks and Services International Academy, Research, and Industry Association (IARIA), volume 8, 2015. [bib]
[2014] CloudTRANSIT - Sichere, plattformunabhängige und transferierbare IT-Services mittles einer generischen Cloud Service Description Language (Nane Kratzke), In ImpulsE - Aus Forschung und Lehre der FH Lübeck, volume 18, 2014. [bib]
[2014] A Lightweight Virtualization Cluster Reference Architecture Derived from Open Source PaaS Platforms (Nane Kratzke), In Open Journal of Mobile Computing and Cloud Computing (MCCC), volume 1, 2014. [bib]
[2014] Lightweight Virtualization Cluster - Howto overcome Cloud Vendor Lock-in (Nane Kratzke), In Journal of Computer and Communication (JCC), volume 2, 2014. [bib]
[2012] An Interdisciplinary Approach on Operational Knowledge Process Modeling and Formal Reasoning (Norbert Gronau, Andreas Kopecny, Nane Kratzke), In Modeling and Analyzing Knowledge Intensive Business Processes with KMDL: Comprehensive Insights Into Theory and Practice GITO mbH Verlag, 2012. [bib]
[2012] Cloud Computing Costs and Benefits (Nane Kratzke), Chapter in Cloud Computing and Services Science Springer New York (Ivan Ivanov, Marten van Sinderen, Boris Shishkov, eds.), 2012. [bib] [pdf]
[2012] Virtual Labs in Higher Education of Computer Science (Nane Kratzke), In Education, volume 2, 2012. [bib] [pdf] [abstract]
Cost efficiency is an often mentioned strength of cloud computing. In times of decreasing educational budgets virtual labs provided by cloud computing might be therefore an interesting option for higher education organizations or IT training facilities. An analysed use case of a web technology lecture and a corresponding practical course of a computer science study programme shows that is not possible to answer the question in general whether cloud computing approaches are economical or not. The general implication of this finding for higher education is, that the application of cloud computing can be only answered from a course specific point of view. This contribution shows why. But also how universities, colleges or other IT training facilities can make profound and course specific decisions for or against cloud based virtual labs from an economic point of view. The presented approach is inspired by Weinmans textquotedblleftmathematical proof of the inevitability of cloud computingtextquotedblright. The key idea is to compare peak to average usage of virtual labs and relate this ratio to costs of classical dedicated labs. The ratio of peak and average usage indicates whether a use case (from a pure economical point of view) is cloud compatible or not. This contribution derives also some findings when cloud computing in higher education has economical advantages or disadvantages. Regarding the analysed use case it turned out that virtual labs are able to provide a more than 25 times cost advantage compared to classical dedicated approaches. Virtual labs can be applied frictionless to classical as well as distance study programmes and virtual labs provide a convenient infrastructure for project as well as problem based learning in computer science. Nevertheless provider of virtual labs should always consider usage and resulting cost characteristics. This article shows how to do this.
[2011] Agent-Based Simulation of Joint Fire Support Teams – Collaboration in Network- Centric Warfare Scenarios (Christian Gerstner, Robert Siegfried, Nane Kratzke), Chapter in Collaborative Agents - Research and Development Springer Berlin Heidelberg (Christian Guttmann, Frank Dignum, Michael Georgeff, eds.), volume 6066, 2011. [bib] [pdf]
[2006] Modell-basierte Identifikation interorganisationaler Wissensflüsse (Nane Kratzke), In Inform., Forsch. Entwickl., volume 20, 2006. [bib]
Konferenz Beiträge
[2018] Towards a Lightweight Multi-Cloud DSL for Elastic and Transferable Cloud-native Applications (Peter-Christian Quint, Nane Kratzke), In Proceedings of the 8th Int. Conf. on Cloud Computing and Services Science (CLOSER 2018), 2018. [bib] [abstract]
Cloud-native applications are intentionally designed for the cloud in order to leverage cloud platform features like horizontal scaling and elasticity – benefits coming along with cloud platforms. In addition to classical (and very often static) multi-tier deployment scenarios, cloud-native applications are typically operated on much more complex but elastic infrastructures. Furthermore, there is a trend to use elastic container platforms like Kubernetes, Docker Swarm or Apache Mesos. However, especially multi-cloud use cases are astonishingly complex to handle. In consequence, cloud-native applications are prone to vendor lock-in. Very often TOSCA- based approaches are used to tackle this aspect. But, these application topology defining approaches are limited in supporting multi-cloud adaption of a cloud-native application at runtime. In this paper, we analyzed several approaches to define cloud-native applications being multi-cloud transferable at runtime. We have not found an approach that fully satisfies all of our requirements. Therefore we introduce a solution proposal that separates elastic platform definition from cloud application definition. We present first considerations for a domain specific language for application definition and demonstrate evaluation results on the platform level showing that a cloud-native application can be transferred between different cloud service providers like Azure and Google within minutes and without downtime. The evaluation covers public and private cloud service infrastructures provided by Amazon Web Services, Microsoft Azure, Google Compute Engine and OpenStack.
[2018] About an Immune System Understanding for Cloud-native Applications - Biology Inspired Thoughts to Immunize the Cloud Forensic Trail (Nane Kratzke), In Proc. of the 9th Int. Conf. on Cloud Computing, GRIDS, and Virtualization (CLOUD COMPUTING 2018, Barcelona, Spain), 2018. [bib]
[2018] About being the Tortoise or the Hare? A Position Paper on Making Cloud Applications too Fast and Furious for Attackers (Nane Kratzke), In Proc. of the 8th Int. Conf. on Cloud Computing and Services Science (CLOSER 2018, Funchal, Madeira, Portugal), 2018. [bib]
[2017] Towards a Description of Elastic Cloud-native Applications for Transferable Multi-Cloud-Deployments (Peter-Christian Quint, Nane Kratzke), In Proceedings of the 1st Int. Forum on Microservices (Microservices 2017, Odense, Denmark), 2017. [bib]
[2017] Smuggling Multi-Cloud Support into Cloud-native Applications using Elastic Container Platforms (Nane Kratzke), In Proceedings of the 7th Int. Conf. on Cloud Computing and Services Science (CLOSER 2017), 2017. [bib]
[2016] Overcome Vendor Lock-In by Integrating Already Available Container Technologies - Towards Transferability in Cloud Computing for SMEs (Peter-Christian Quint, Nane Kratzke), In Proceedings of CLOUD COMPUTING 2016 (7th. International Conference on Cloud Computing, GRIDS and Virtualization), 2016. [bib]
[2016] ppbench - A Visualizing Network Benchmark for Microservices (Nane Kratzke, Peter-Christian Quint), In Proceedings of the 6th International Conference on Cloud Computing and Services Science (CLOSER 2016), 2016. [bib] [abstract]
Companies like Netflix, Google, Amazon, Twitter successfully exemplified elastic and scalable microservicearchitectures for very large systems. Microservice architectures are often realized in a way to deploy servicesas containers on container clusters. Containerized microservices often use lightweight and REST-based mech-anisms. However, this lightweight communication is often routed by container clusters through heavyweightsoftware defined networks (SDN). Services are often implemented in different programming languages addingadditional complexity to a system, which might end in decreased performance. Astonishingly it is quite com-plex to figure out these impacts in the upfront of a microservice design process due to missing and specializedbenchmarks. This contribution proposes a benchmark intentionally designed for this microservice setting. Weadvocate that it is more useful to reflect fundamental design decisions and their performance impacts in theupfront of a microservice architecture development and not in the aftermath. We present some findings regard-ing performance impacts of some TIOBE TOP 50 programming languages (Go, Java, Ruby, Dart), containers(Docker as type representative) and SDN solutions (Weave as type representative).
[2015] About Microservices, Containers and their Underestimated Impact on Network Performance (Nane Kratzke), In Proceedings of CLOUD COMPUTING 2015 (6th. International Conference on Cloud Computing, GRIDS and Virtualization), 2015. [bib]
[2012] Are Cloud Enabled Virtual Labs Economical? - A Case Study Analyzing Cloud based Virtual Labs for Educational Purposes (Nane Kratzke), In CSEDU (2), 2012. [bib]
[2012] What Cost Us Cloud Computing? - A Case Study on How to Decide for or against IaaS based Virtual Labs (Nane Kratzke), In CLOSER, 2012. [bib]
[2011] Overcoming Ex Ante Cost Intransparency of Clouds - Using System Analogies and a Corresponding Cost Estimation Model (Nane Kratzke), In CLOSER, 2011. [bib]
[2011] Cloud-based IT Management Impacts - Qualitative Weaknesses and Strengths of Clouds (Nane Kratzke), In CLOSER, 2011. [bib]
[2009] Monitoring of reliability in Bayesian identification (Max Krüger, Nane Kratzke), In FUSION, 2009. [bib]
[2009] How Do System and Enterprise Architectures Influence Knowledge Management in Software Projects? - An Explorative Study of Six Software Projects (Carsten Lucke, Markus May, Nane Kratzke, Ulrike Lechner), In EMISA, 2009. [bib]
[2005] Ganzheitliche Entwicklungsbegleitung komplexer Waffensysteme der Marine (Nane Kratzke), In missing booktitle, volume GI Jahrestagung (2), 2005. [bib]
Workshop Beiträge
[2016] ClouNS - A Reference Model for Cloud-Native Applications (Nane Kratzke, Rene Peinl), In Proceedings of 20th. International Conference on Enterprise Distributed Object Computing Workshops (EDOCW 2016), 2016. [bib]
[2010] Collaboration in Network-Centric Warfare Modeling Joint Fire Support Teams (Christian Gerstner, Robert Siegfried, Nane Kratzke), In Web Intelligence/IAT Workshops, 2010. [bib]
Sonstige Veröffentlichungen
[2018] Preliminary Technical Report of Project CloudTRANSIT - Transfer Cloud-native Applications at Runtime (Nane Kratzke, Peter-Christian Quint), Technical report, The Center of Excellence CoSA, Lübeck University of Applied Sciences, 2018. [bib]
[2017] 80% der Cloud-Dienste sind nicht standardisiert (Nane Kratzke, Thomas Hafen), Interview Neue Mediengesellschaft Ulm mbH, volume 5, 2017. [bib]
[2017] Analyse und Integration von Storage-Clustern in elastische Container Plattformen (Thomas Finnern), Master's thesis, Luebeck University of Applied Sciences, 2017. [bib]
[2016] Evaluation einer Cloudspeicher-Loesung bei einem Telekommunikationsunternehmen (Thomas Finnern), Technical report, Fachhochschule Lübeck, 2016. [bib] [pdf]
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