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Automation is the key to enable an efficient, fast, and reliable deployment of applications. Therefore, several deployment automation technologies emerged in recent years whereby each technology has its specific field of application: While some are bound to cloud providers and offer provider-specific functionalities, others enable multi-cloud deployments but mostly do not support provider-specific features. As a consequence, often companies have to use multiple deployment technologies in combination to deploy large applications. However, the management capabilities of most deployment technologies are limited or even non-existent. This issue becomes even more severe if different parts of a single application are deployed by different technologies. To tackle this issue, we present an approach that enables generating automatically executable management workflows for applications that consist of multiple components deployed by different deployment technologies. Our approach builds on top of instance models that are automatically generated based on information retrieved from the different deployment technologies involved. Based on the derived instance model, we generate workflows that manipulate the running application. We prove the technical feasibility by an open-source prototype and discuss a detailed case study.

Quantum applications are hybrid, i.e., they comprise quantum and classical programs, which must be orchestrated. Workflows are a proven solution for orchestrating heterogeneous programs while providing benefits, such as robustness or scalability. However, the orchestration using workflows can be inefficient for some quantum algorithms, requiring the execution of quantum and classical programs in a loop. Hybrid runtimes are offered to efficiently execute these algorithms. For this, the quantum and classical programs are combined in a single hybrid program, for which the execution is optimized. However, this leads to a conceptual gap between the modeling benefits of workflow technologies, e.g., modularization, reuse, and understandability, and the efficiency improvements when using hybrid runtimes. To close this gap, we introduce a method to model all tasks explicitly in the workflow model and analyze the workflow to detect parts of the workflow that can benefit from hybrid runtimes. Furthermore, corresponding hybrid programs are automatically generated based on the quantum and classical programs, and the workflow is rewritten to invoke them. To ease the live monitoring and later analysis of workflow executions, we integrate process views into our method and collect related provenance data. Thus, the user can visualize and monitor the workflow in the original and rewritten form within the workflow engine. The practical feasibility of our approach is validated by a prototypical implementation, a case study, and a runtime evaluation.

Um die derzeit vorherrschende Diskrepanz zwischen wissenschaftlicher Forschung und Anwendungen von Quantencomputing zu tilgen, will die Bundesregierung mit breiten Unterstutzungen der Wirtschaft unter die Arme greifen. Doch nun fragt es sich, wie, wo und in welchem Mas man in neue Technologien investiert, um sich im globalen Wettrusten von Quantencomputing bestmoglich zu positionieren. Frank Leymann ist Professor und Institutsleiter am Institut fur Architektur von Anwendungssystemen an der Universitat Stuttgart. Durch seine langjahrige praktische Erfahrung in der Wirtschaft versucht er, den Bogen zwischen Forschung und Praxis zu spannen.

In recent years, several technologies were developed enabling the automated provisioning and decommissioning of cloud applications. To reduce costs, these applications can be terminated and restarted on demand. However, as an application is terminated, its current application state, i.e., all application specific data is deleted along with the running application instance. This application state may be holding all business-critical information, and, hence, must be saved before the application is terminated. One possibility to save application states is to create VM snapshots although this is not always possible or sufficient. Therefore, we introduce two approaches: (1) a concept to generically terminate applications and save their internal state, and (2) an approach to reinstate the application in the same state again. To evaluate their practical feasibility, we implemented a proof of concept in the OpenTOSCA ecosystem.

The establishment of digital humanities as a research field has shown that the use of computers as tools, but also the use of methods and techniques from computer science, can contribute enormously to research done in the humanities. Since quantum computers are expected to become generally available in the next few years, it is promising to use the advantages of this new technology for addressing existing as well as completely new questions in the humanities. The use of quantum computers offers a great deal of potential: (i) they are much faster than classical computers in solving certain complex problems, (ii) solutions may be much more precise, (iii) they allow the solution of problem classes that can hardly been solved on classical computers, and (iv) their usage promises to be much cheaper than that of supercomputers. Parts of this potential are already used in different domains but are also particular promising for digital humanities research. This paper outlines the vision of such quantum humanities.

Traditional distributed transaction processing (TP) systems, such as replicated databases, faced difficulties in getting wide adoption for scenarios of enterprise integration due to the level of mutual trust required. Ironically, public blockchains, which promised to solve the problem of mutual trust in collaborative processes, suffer from issues like scalability, probabilistic transaction finality, and lack of data confidentiality. To tackle these issues, permissioned blockchains were introduced as an alternative approach combining the positives of the two worlds and avoiding their drawbacks. However, no sufficient analysis has been done to emphasize their actual capabilities regarding TP. In this paper, we identify a suitable collection of TP criteria to analyze permissioned blockchains and apply them to a prominent set of these systems. Finally, we compare the derived properties and provide general conclusions., Comment: 12 pages. This is a pre-print of an article published in SICS Software-Intensive Cyber-Physical Systems. The final authenticated version is available online at: https://doi.org/10.1007/s00450-019-00411-y

In recent years, a plethora of deployment technologies evolved, many following a declarative approach to automate the delivery of software components. Even if such technologies share the same purpose, they differ in features and supported mechanisms. Thus, it is difficult to compare and select deployment automation technologies as well as to migrate from one technology to another. Hence, we present a systematic review of declarative deployment technologies and introduce the Essential Deployment Metamodel (EDMM) by extracting the essential parts that are supported by all these technologies. Thereby, the EDMM enables a common understanding of declarative deployment models by facilitating the comparison, selection, and migration of technologies. Moreover, it provides a technology-independent baseline for further deployment automation research.

Pattern languages as tools for solving problems based on interconnected, abstract, and proven solutions can offer valuable help to practitioners. But there is always the question of where to begin when a pattern language should be applied. Their authors often provide entry points, but these are usually only useful if one starts completely from scratch or from a very specific situation. When confronted with problems at hand, practitioners are often left to find a suitable entry point themselves by reading through the whole pattern language to find applicable patterns. To help with this problem, we present a general approach and its formalization that provides entry points for any kind of situation. Our general three step approach guides practitioners through Situation Assessment, Treatment Selection, and Treatment Application in order to find and apply a suitable pattern language for their specific problems. We formalize all the parts involved and show that the facts collected during Situation Assessment can be used to find a suitable entry point for a specific situation. We also present an algorithm for finding these entry points.

With the continuous growth of IT application systems, the complexity of architecture modeling and development increases. Patterns document proven solutions for recurring problems in an abstract and human readable manner. Within the domain of IT architectures, they should support the architecture modeling and development process. Due to the documentation of patterns as text documents, they cannot be applied to an architecture automatically: patterns have to be read, understood, adapted to the corresponding use case, and realized manually over and over again. Consequently, architecture modeling considering the variety of documented patterns becomes even more complex and time-consuming. To tackle these issues, we introduce an approach and for an automated application and realization of patterns in architectural graphs using graph transformation techniques. This eases the architecture modeling process in which proven solutions for recurring problems can be selected as required and applied automatically. We present the concept, formalization, and validate our approach based on an application example.

For automating the deployment of applications in cloud environments, a variety of technologies have been developed in recent years. These technologies enable to specify the desired deployment in the form of deployment models that can be automatically processed by a provisioning engine. However, the deployment across several clouds increases the complexity of the provisioning. Using one deployment model with a single provisioning engine, which orchestrates the deployment across the clouds, forces the providers to expose low-level APIs to ensure the accessibility from outside. In this paper, we present an extended version of the split and match method to facilitate the division of deployment models to multiple models which can be deployed by each provider separately. The goal of this approach is to reduce the information and APIs which have to be exposed to the outside. We present a formalization and algorithms to automate the method. Moreover, we validate the practical feasibility by a prototype based on the TOSCA standard and the OpenTOSCA ecosystem.

For the automated deployment of applications, technologies exist which can process topology-based deployment models that describes the application’s structure with its components and their relations. The topology-based deployment model of an application can be adapted for the deployment in different environments. However, the structural changes can lead to problems, which had not existed before and prevent a functional deployment. This includes security issues, communication restrictions, or incompatibilities. For example, a formerly over the internal network established insecure connection leads to security problems when using the public network after the adaptation. In order to solve problems in adapted deployment models, first the problems have to be detected. Unfortunately, detecting such problems is a highly non-trivial challenge that requires deep expertise about the involved technologies and the environment. In this paper, we present (1) an approach for detecting problems in deployment models using architecture and design patterns and (2) the automation of the detection process by formalizing the problem a pattern solves in a certain context. We validate the practical feasibility of our approach by a prototypical implementation for the automated problem detection in TOSCA topologies.

The blockchain is an emerging technology that allows multiple parties to agree on a common state without the need for trusted intermediaries. Moreover, business process technology streamlines the automation of inter- and intra-organizational processes while cutting-down on costs. With the new business opportunities provided by blockchains, it becomes vital to combine both technologies to allow the modeling and execution of blockchain-based interactions within business processes. However, the existing business process modeling languages lack support to intuitively model the various interactions with blockchains. In this paper we address this issue by proposing a business process modeling extension that captures the particularities of blockchains. We also show how to transform the proposed constructs into standard-compliant models, and we present an integration architecture that allows external applications, to communicate with the blockchains. Finally, we validate our approach by providing a prototypical implementation that proves its practical feasibility.

Pattern languages are a pervasive means in many domains to capture proven solutions for recurring problems in an abstract manner. To improve reusability, they abstract from implementation details such as specific technologies or environments. However, while this abstraction provides a significant benefit as patterns can be applied to solve different manifestations of the general problem, this also leads to time-consuming efforts when patterns have to be applied as concrete solutions have to be elaborated and implemented over and over again. Moreover, as patterns are intended to be applied in combination with other patterns, the individual concrete solutions have to be aggregated into an overall solution, too. However, this immensely increases necessary expertise, required effort, and complexity. Therefore, we present a systematic approach that allows to (i) reuse and (ii) combine already developed concrete solutions on the basis of selected sequences of patterns. We establish the theory of solution algebras, which perceive concrete solutions and aggregation operators as mathematical objects. Thereby, domain-specific operators allow to combine and aggregate concrete solutions of patterns, which we validate in several different domains.

Today, REST APIs have established as a means for realizing distributed systems and are supposed to gain even more importance in the context of Cloud Computing, Internet of Things, and Microservices. Nevertheless, many existing REST APIs are known to be not well designed, resulting in the absence of desirable non-functional properties that truly RESTful systems entail. Although existing analysis show, that many REST APIs are not fully REST compliant, it is still an open issue how to improve this deficit and where to start. In this work, we apply structural analysis of REST APIs in order to support API governance, resulting in a set of basic and aggregated metrics that characterize an API set and also guide further governance tasks. We apply the structural analysis on a set of 286 real world APIs and then demonstrate how to derive suitable metrics that represent the perceived complexity of an API, complemented and validated by a survey of developers following the AHP process. As a result, we provide effective support for API governance, helping to identify and remedy problems in APIs.

Due to recent advances in data science and Big Data the importance of data is increasing. Although service choreographies provide means to specify complex conversations between multiple interacting parties from a global perspective and in a technology-agnostic manner, they do not fully reflect the paradigm shift towards data-awareness at the moment. In this paper, we discuss current shortcomings such as missing support for data flow across services and a choreography data contract all interacting parties agree on. This results in more complex and rigid choreography models, making them also less flexible regarding their data perspective during run time. The main contribution is our approach for modeling and execution of data-aware service choreographies towards increasing the level of data awareness in choreographies.

Collaboration is a key aspect for establishing DevOps-oriented processes because diverse experts such as developers and operations personnel need to efficiently work together to deliver applications. For this purpose, highly automated continuous delivery pipelines are established, consisting of several stages and their corresponding application environments (development, test, production, etc.). The DevOps community provides a huge variety of tools and reusable artifacts (i.e. DevOps solutions such as deployment engines, configuration definitions, container images, etc.) to implement such application environments. This paper presents the concept of collaborative solution repositories, which are based on established software engineering practices. This helps to systematically maintain and link diverse solutions. We further discuss how discovery and capturing of such solutions can be automated. To utilize this knowledge (made of linked DevOps solutions), we apply continuous delivery principles to create diverse knowledge base instances through corresponding pipelines. Finally, an integrated architecture is outlined and validated using a prototype implementation.

Costumes are prominent in transporting a character's mood, a certain stereotype, or character trait in a film. The concept of patterns, applied to the domain of costumes in films, can help costume designers to improve their work by capturing knowledge and experience about proven solutions for recurring design problems. However, finding such Costume Patterns is a difficult and time-consuming task, because possibly hundreds of different costumes of a huge number of films have to be analyzed to find commonalities. In this paper, we present a Semi-Automated Costume Pattern Mining Method to discover indicators for Costume Patterns from a large data set of documented costumes using data mining and data warehouse techniques. We validate the presented approach by a prototypical implementation that builds upon the Apriori algorithm for mining association rules and standard data warehouse technologies.

Investigating the emotional impact of historical music, e.g. music of the 19th century, is a complex challenge since the subjects that listened to this music and their emotions are forever gone. As a result, asking them for their experiences is not possible anymore and we need other means to gain insights into the expressive quality of music of this century. In this vision paper, we describe a pattern-based method called MUSE4Music to quantitatively find similarities in different pieces of music. The reconstruction of musical patterns will allow us to draw conclusions from erratic documents that go far beyond the single pieces they are referring to.

One of the core principles in service oriented computing is that services are always on and available. There are however domains where running services all the time is not suitable, for example when applying simulation workflows in the eScience domain. The simulation services orchestrated by these workflows are typically used only rarely and irregularly, keeping them running all the time would result in a significant waste of resources. As a consequence, we developed the approach of on-demand provisioning of workflow middleware and services. In this paper we will give an overview about our work. We will present the motivation and main idea of our solution approach and will also provide details about some of the results of our work. The overview about our previous and current work is then complemented by a detailed discussion and comparison of the roles involved in both concepts, traditional service oriented computing as well as our newly developed on-demand provisioning approach.

eHealth gains more and more interest since a lot of end-user devices supporting health data capturing are available. The captured data has to be managed and securely stored, in order to access it from different devices and share it with other users such as physicians. The aim of the german-greek research project ECHO is to support the treatment of patients, who suffer from chronic obstructive pulmonary disease, a chronic respiratory disease. Usually the patients need to be examined by their physicians on a regular basis due to their chronic condition. Since this is very time consuming and expensive we developed an eHealth system which allows the physician to monitor patients condition remotely, e.g., via smart phones. This article is an extension of previous work, where we introduced a health data model and an associated platform-architecture for the management and analysis of the data provided by the patients. There we have also shown how the security of the data is ensured and we explained how the platform can be provided in a cloud-based environment using the OASIS standard TOSCA, which enables a self-contained management of cloud-services. In this article we provide a more detailed description about the health data analysis, provisioning and security aspects of the eHealth system.

Today, the Internet of Things has evolved due to an advanced interconnectivity of hardware devices equipped with sensors and actuators. Such connected environments are nowadays well-known as smart environments. Famous examples are smart homes, smart cities, and smart factories. Such environments should only be called "smart" if they allow monitoring and self-organization. However, this is a great challenge: (1) sensors have to be bound and sensor data have to be efficiently provisioned to enable monitoring of these environments, (2) situations have to be detected based on sensor data, and (3) based on the recognized situations, a reaction has to be triggered to enable self-organization, e.g., through notification delivery or the execution of workflows. In this article, we introduce SitOPT--an approach for situation recognition based on raw sensor data and automated handling of occurring situations through notification delivery or execution of situation-aware workflows. This article is an extended version of the paper "SitRS--Situation Recognition based on Modeling and Executing Situation Templates" presented at the 9th Symposium and Summer School of Service-oriented Computing 2015.

Facilitating compliance management, that is, assisting a company's management in conforming to laws, regulations, standards, contracts, and policies, is a hot but non-trivial task. The service-oriented architecture (SOA) has evolved traditional, manual business practices into modern, service-based IT practices that ease part of the problem: the systematic definition and execution of business processes. This, in turn, facilitates the online monitoring of system behaviors and the enforcement of allowed behaviors--all ingredients that can be used to assist compliance management on the fly during process execution. In this paper, instead of focusing on monitoring and runtime enforcement of rules or constraints, we strive for an alternative approach to compliance management in SOAs that aims at assessing and improving compliance. We propose two ingredients: (i) a model and tool to design compliant service-based processes and to instrument them in order to generate evidence of how they are executed and (ii) a reporting and analysis suite to create awareness of a company's compliance state and to enable understanding why and where compliance violations have occurred. Together, these ingredients result in an approach that is close to how the real stakeholders--compliance experts and auditors--actually assess the state of compliance in practice and that is less intrusive than enforcing compliance.

The migration of existing applications to the Cloud requires adapting them to a new computing paradigm. Existing works have focused on migrating the whole application stack by means of virtualization and deployment on the Cloud, delegating the required adaptation effort to the level of resource management. With the proliferation of Cloud services allowing for more flexibility and better control over the application migration, the migration of individual application layers, or even individual architectural components to the Cloud, becomes possible. Towards this goal, in this work we focus on the challenges and solutions for each layer when migrating different parts of the application to the Cloud. We categorize different migration types and identify the potential impact and adaptation needs for each of these types on the application layers based on an exhaustive survey of the State of the Art. We also investigate various cross-cutting concerns that need to be considered for the migration of the application, and position them with respect to the identified migration types. Finally, we present some of the open research issues in the field and position our future work targeting these research questions.

Business processes are a key aspect of modern organization. In recent years, business process management and optimization has been applied to different cross-cutting concerns such as security, compliance, or Green IT, for example. Based on the ecological characteristics of a business process, proper environmentally sustainable adaptation strategies can be chosen to improve the total environmental impact of the business process. We use ecological sustainable adaptation strategies that are described as green business process patterns. The application of such a green business process pattern, however, affects the business process layer, the application component and the infrastructure layer. This implies that changes in the application infrastructure also need to be considered. Hence, we use best practices of cloud application architectures which are described as Cloud patterns. To guide developers through the adaptation process we propose a pattern-based approach in this work. We correlate Cloud patterns relevant for sustainable business processes to green business process patterns and organize them within a classification. To provide concrete implementation support we further annotate these Cloud patterns to application component models that are described with the topology and orchestration specification for cloud applications (TOSCA). Using these annotations, we describe a method that provides the means to optimize business processes based on green business process patterns through adapting the implementation of application components with concrete TOSCA implementation models.

Business Process Execution Language for Web Services (BPEL4WS) allows defining both, business processes that make use of Web services, and business processes that externalize their functionality as Web services. This short paper introduces the basic language elements of BPEL4WS using a simple example. The concepts underlying the language are briefly explained: establishing bilateral partnerships, correlating messages and processes, defining the order of the activities of a business process, event handling, handling exceptions via long-running transactions, and the usage of BPEL4WS in pure B2B scenarios. The paper finishes off by presenting the resulting application structure and the resulting economical implications.

Aus der Verwendung von Workflow-Management-Systemen in der Praxis ergeben sich drei wesentliche Anforderungen an die Transaktionsunterstutzung fur Workflows: Zunachst mus das WFMS selbst Manipulationen seiner eigenen Ressourcen unter Transaktionsschutz durchfuhren. Weiterhin ist die Moglichkeit des gesicherten Aufrufs von Transaktionen und deren Gruppierung zu verteilten Transaktionen zu ermoglichen. Schlieslich ist die Unterstutzung von langen Transaktionen zu gewahrleisten, die sich aus der Semantik der abgebildeten Geschaftsprozesse ergeben.