Context: Rapid developments and adoption of machine learning-based software solutions have enabled novel ways to tackle our societal problems. The ongoing digital transformation has led to the incorporation of these software solutions in just about every application domain. Software architecture for machine learning applications used during sustainable digital transformation can potentially aid the evolution of the underlying software system adding to its sustainability over time. Objective: Software architecture for machine learning applications in general is an open research area. When applying it to sustainable digital transformation it is not clear which of its considerations actually apply in this context. We therefore aim to understand how the topics of sustainable digital transformation, software architecture, and machine learning interact with each other. Methods: We perform a systematic mapping study to explore the scientific literature on the intersection of sustainable digital transformation, machine learning and software architecture. Results: We have found that the intersection of interest is small despite the amount of works on its individual aspects, and not all dimensions of sustainability are represented equally. We also found that application domains are diverse and include many important sectors and industry groups. At the same time, the perceived level of maturity of machine learning adoption by existing works seems to be quite low. Conclusion: Our findings show an opportunity for further software architecture research to aid sustainable digital transformation, especially by building on the emerging practice of machine learning operations.

Infrastructure as Code (IaC) solutions such as HashiCorp’s Terraform and Amazon Web Services’ CloudFormation are invaluable tools in dealing with the increasing complexity of deploying software systems continuously on the cloud, and the need to manage larger and more complex infrastructures. However, not many existing works have examined the cost implications of IaC adoption for cloud-based software systems. In this work, we apply thematic analysis to 2289 file diffs, spanning 828 commits from 618 repositories, to identify recurring solutions and ineffective practices in cost management of Terraform and CloudFormation artifacts. We uncover a catalog of five patterns and seven antipatterns, and analyze their (co-)occurrences. Our results indicate that many teams address cost reactively by making incremental fixes, while others take proactive steps such as configuring budgets, integrating cost reports, and designing preventative templates. To aid practitioners in catching these issues early, we also present a linter as an extension of the Checkov tool that automates detection of selected (anti)patterns in Terraform and CloudFormation files, offering real-time feedback on potential oversights. We evaluated the utility of this tool by applying it to 182 active open-source repositories and soliciting practitioner feedback. Our results reveal that while cost-related misconfigurations are widespread, developer engagement is limited, suggesting that cost optimization is often prioritized lower than security or functionality and addressed reactively. Together, this catalog and detection tool provide actionable insights for optimizing cloud resource management, enhancing cost efficiency, and fostering informed decision-making in cloud deployments.

The ever-increasing use of cloud computing in software systems necessitates considering the sustainability implications of cloud-based software from the perspective of software architecture. Software architecture for these purposes is the set of decisions with lasting, system-wide effect on the system’s software elements, the relations among them, and the properties of both. In this context, understanding the implications of cloud adoption for the sustainability of software systems from the perspective of software practitioners is the key objective of this study. Specifically, we aim to determine the type of sustainability impact cloud adoption has on the architecture of these systems across different dimensions and associated quality requirements. To achieve our goal, we employ mining software repository techniques along with qualitative analysis on a constructed dataset on the Software Engineering, Software Quality Assurance and Testing, and DevOps forums of the Stack Exchange Q&A platform. Our results confirm findings from previous work, showing a strong perceived positive effect of cloud computing on quality requirements and sustainability dimensions from the perspective of practitioners. Additionally, we identify 9 new quality requirements frequently employed in cloud-based architectures and their contributions to sustainability dimensions.

As the dependence on software systems running on cloud data centers grows on a daily basis, there is an increasingly stronger motivation to reduce their energy consumption. A necessary but not trivial step in this direction is understanding how energy is consumed in virtualized, multi-tenant environments such as the one provisioned in the cloud. Prior work focuses on isolated, non-virtualized systems and is difficult to transfer to this context. A number of industry-led approaches have appeared in the meantime in terms of tools and technological stacks building on the concept of observability as the means to achieve this goal. This paper discusses our approach in adopting one such stack and consequently assessing it for fitness to purpose through an experimental procedure. To this effect, we deploy a cloud-native application on a private cloud infrastructure instrumented for measuring energy consumption through a combination of hardware and software means. We combine the information from these instrumentation points into a mapping model to deal with the different virtualization layers and compare the model against the values reported by the observability stack. Furthermore, we use our model to attribute energy consumption across the virtualization layers and understand how energy is consumed at each one.

Cloud adoption is historically driven by cost considerations. As the complexity of the software systems deployed on the cloud continuously increases, and with it also the need to manage larger and more complex infrastructures, Infrastructure as Code (IaC) approaches become invaluable tools. However, not many existing works have looked into the cost implications of IaC use for cloud-based software. In this work we build on an existing dataset that has looked into cost-related commits on IaC artifacts in open-source repositories in order to identify recurring solutions and ineffective practices in cost management. We present a catalog of patterns and antipatterns organizing our findings, and discuss its implication for practitioners and researchers.

The serverless cloud computing execution model presents an opportunity for implementing data processing pipelines in a cost efficient manner with minimum or no consideration about managing their operational aspects. In doing so, however, a major concern that emerges is the adoption of provider-specific languages required for orchestrating serverless functions and supporting services into the higher order logic of pipelines. As a result, the vendor lock-in problem inherent to cloud computing becomes further intensified. In this work we propose an approach to mitigate this issue through the adoption and adaptation of well-known patterns from the literature as the means for modeling the pipeline logic. By providing mappings from these patterns to vendor-specific orchestration language constructs we allow for efficient realization of provider-agnostic pipeline models as provider-specific executable workflows. An industrial case study provides evidence towards the suitability of our proposal for practical purposes.

The interest in the intersection between cloud computing and sustainability is naturally growing as the popularity of the former makes it in many cases the default model for delivering software functionalities to end users. Furthermore, software architecture offers a fundamental route to address sustainability, with the recent shift towards recognizing sustainability as a software quality. Approaching the intersection between sustainability and cloud computing from the perspective of the study of software architectures, in this work we aim to collect the data necessary for us to understand the relation between these two areas as reflected in the literature. Given the lack of suitable surveys for this purpose in this paper we report on our review of the relevant literature, designed to address the question of how architectural solutions specifically for the cloud are addressing sustainability. Following the steps prescribed by a well-known method for this purpose on running a systematic literature review, we answer our defined research questions using both (qualitative) analysis and synthesis of data. As a result of the review process, we are able to identify 10 solution types, 8 recurring design decisions, 11 involved architectural entities, and 17 distinct perceptions of the cloud with respect to sustainability. The adoption of reusable architectural tactics and patterns has been observed to be less prevalent than anticipated. Furthermore, certain fundamental characteristics of cloud computing, including multi-tenancy, on-demand resource provisioning, and the pay-as-you-go model, have been identified across diverse cloud perceptions. Our findings point to the need for more systematic work required on developing architectural solutions specifically for cloud computing incorporating sustainability goals. We also suggest that achieving sustainability through cloud in software architecture may be feasible. Furthermore, we identify a persistent threat to further secondary studies on the same topic due to improper use of terms.

Cloud computing’s rise as the primary platform for software development and delivery is largely driven by the potential cost savings. However, it is surprising that no empirical evidence has been collected to determine whether cost awareness permeates the development process and how it manifests in practice. This study aims to provide empirical evidence of cost awareness by mining open source repositories of cloud-based applications. The focus is on Infrastructure-as-Code artifacts that automate software (re)deployment on the cloud. A systematic examination of 152735 repositories yielded 2010 relevant hits. We then analyzed 538 relevant commits and 208 relevant issues using inductive and deductive coding and corroborated findings with discussions from Stack Overflow. The findings indicate that developers are not only concerned with the cost of their application deployments but also take actions to reduce these costs beyond selecting cheaper cloud services. We also identify research areas for future consideration. Although we focus on a particular Infrastructure-as-Code technology (Terraform), the findings can be applicable to cloud-based application development in general. The provided empirical grounding can serve developers seeking to reduce costs through service selection, resource allocation, deployment optimization, and other techniques.

Abstract Context The management of Interface Control Documents (ICDs) has shown to be a major pain point in the architecting processes of Systems of Systems (SoS). Objective This work aims to improve on previously identified ICD management issues using the documentation-as-code philosophy as a potential basis for a treatment, and in collaboration with practitioners. Method We conducted a Technical Action Research (TAR) study with a group of engineers at the Netherlands Radio Astronomy Institute (ASTRON), in the context of the LOFAR radio telescope. An additional research instrument, in the form of an expert panel, was used to evaluate the transferability of the proposed treatment to alternative domains. Results In-depth insights on previously identified interface management issues were gained. Based on these insights a functional proof-of-concept was developed aimed at addressing these issues following the documentation-as-code principles. In addition to receiving overall positive reviews from practitioners and experts, further areas of improvement and transferability considerations for future work were identified. Conclusions The proposed approach, which to our knowledge has not been explored before in this context, is promising to address some of the recurring interfacing-related issues with directed SoS in multiple engineering domains. This could be done mainly by enforcing consistency and completeness on both text-based and formal elements of the ICDs, and turning ICDs into single sources of truth for the architecting processes of large scale SoS.

Sustainability is an increasingly-studied topic in software engineering in general, and in software architecture in particular. There are already a number of secondary studies addressing sustainability in software engineering, but no such study focusing explicitly on software architecture. This work aims to fill this gap by conducting a systematic mapping study on the intersection between sustainability and software architecture research with the intention of (i) reflecting on the current state of the art, and (ii) identifying the needs for further research. Our results show that, overall, existing works have focused disproportionately on specific aspects of sustainability, and in particular on the most technical and “inward facing” ones. This comes at the expense of the holistic perspective required to address a multi-faceted concern such as sustainability. Furthermore, more reflection-oriented research works, and better coverage of the activities in the architecting life cycle are required to further the maturity of the area. Based on our findings we then propose a research agenda for sustainability-aware software architecture.

The challenges posed by the architecting of System of Systems (SoS) has motivated a significant number of research efforts in the area. However, literature is lacking when it comes to the interplay between the disciplines involved in the architecting process, a key factor in addressing these challenges. This paper aims to contribute to this line of research by confirming and extending previously characterized architecting harmonization practices from Systems and Software Engineering, adopted in an ultra-large-scale SoS. We conducted a confirmatory case study on the Square-Kilometre Array (SKA) project to evaluate and extend the findings of our exploratory case on the LOFAR/LOFAR2.0 radio-telescope projects. In doing so, a pre-study was conducted to map the findings of the previous study with respect to the SKA context. A survey was then designed, through which the views of 46 SKA engineers were collected and analyzed. The study confirmed in various degrees the four practices identified in the exploratory case, and provided further insights about them: (1) the friction between disciplines caused by long-term system requirements, and how they can be ameliorated through intermediate, short-term requirements; (2) the way design choices with a cross-cutting impact on multiple agile teams have an indirect impact on the system architecture; (3) how these design choices are often caused by the criteria that guided early system decomposition; (4) the seemingly recurrent issue with the lack of details about the dynamic elements of the interfaces; and (5) the use of machine-readable interface specifications for aligning hardware/software development processes. The findings of this study and its predecessor support the importance of a cross-disciplinary view in the Software Engineering research agenda in SoS as a whole, not to mention their value as a convergence point for research on SoS architecting from the Systems and Software Engineering standpoints.

The problems caused by the gap between system- and software-level architecting practices, especially in the context of Systems of Systems where the two disciplines inexorably meet, is a well known issue with a disappointingly low amount of works in the literature dedicated to it. At the same time, organizations working on Systems of Systems have been developing solutions for closing this gap for many years now. This work aims to extract such knowledge from practitioners by studying the case of a large-scale scientific instrument, a geographically distributed radio telescope to be more specific, developed as a sequence of projects during the last two decades. As the means for collecting data for this study we combine online interviews with a virtual focus group of practitioners from the organization responsible for building the instrument. Through this process, we identify persisting problems and the best practices that have been developed to deal with them, together with the perceived benefits and drawbacks of applying the latter in practice. Some of our major findings include the need to avoid over-reliance on the flexibility of software to compensate for incomplete requirements, hidden assumptions, as well as late involvement of system architecting, and to facilitate the cooperation between the involved disciplines through dedicated architecting roles and the adoption of unifying practices and standards.

Background: The Systems Engineering and Software Engineering disciplines are highly intertwined in most modern Systems of Systems (SoS), and particularly so in industries such as defense, transportation, energy and health care. However, the combination of these disciplines during the architecting of SoS seems to be especially challenging; the literature suggests that major integration and operational issues are often linked to ambiguities and gaps between system-level and software-level architectures.

Cloud-enabled Cyber Physical Systems (CCPS) combine embedded systems with highly scalable cloud services. Such systems provide opportunities to offload computing or data analytics tasks which require more resources than an embedded device can offer. The development of a CCPS involves multiple stakeholders as well as engineers and developers from different disciplines, which makes the description and communication of the system architecture a challenging task. Additionally, the architecture design of CCPS has the inherent challenge to determine which functionality should be placed on the device, in the cloud, or on a possible fog/edge device within or close to the system. This systematic mapping study evaluates how CCPS architectures are discussed in the current literature and which topics are associated with cloud computing in CCPS architectures. The results show a significant increase in CCPS publications over the last years, a focus on a specific architectural viewpoint and application areas, and a potential misalignment with the common understanding of cloud computing as a paradigm.

Function as a Service (FaaS) is an integral part of the serverless computing paradigm. It offers a true pay-per-use billing model and releases developers from the burden of managing the application stack. A discussion on whether and when this model is more appropriate for cloud computing users in terms of accruing costs compared to the more "traditional" delivery models has already been started by existing works. However, by treating this subject as a regular service selection problem, these approaches fail to exploit the space created by distributing the load between simultaneous FaaS and non-FaaS deployments of an application in a hybrid deployment model. This work aims to provide the means for application owners to decide which deployment scenario is cost optimal for their needs. In case this scenario is a hybrid deployment, the proposed approach also determines the optimal number of virtual machines that will need to be provisioned. An extensible and configurable FaasSimulator open source tool is presented for these purposes.

Context: The term System of Systems (SoS) has increasingly been used in a wide variety of domains to describe those systems composed of independent constituent systems that collaborate towards a mission that they could not accomplish on their own. There is a significant volume of research by the software architecture community that aims to overcome the challenges involved in architecting SoS, as evidenced by the number of secondary studies in the field published so far. However, the boundaries of such research do not seem to be well defined, at least partially, due to the emergence of SoS-adjacent areas of interest like the Internet of Things . Objective: This paper aims to investigate the current state of research on SoS architecting by synthesizing the demographic data, assessing the quality and the coverage of architecting activities and software quality attributes by the research, and distilling a concept map that reflects a community-wide understanding of the concept of SoS. Method: We conduct what is, to the best of our understanding, the first tertiary study on SoS architecting. Such tertiary study was based on five research questions, and was performed by following the guidelines of Kitchenham et al. In all, 19 secondary studies were evaluated, which is comparable to other tertiary studies. Results: The study illustrates a state of disconnection in the research community, with research gaps in the coverage of particular phases and quality attributes. Furthermore, a more effective approach in classifying systems as SoS is required, as the means of resolving conceptual and terminological overlaps with the related domains. Conclusions: Despite the amount of research in the area of SoS architecting, more coordinated and systematic targeted efforts are required in order to address the identified issues with the current state of research.

Scientists are increasingly using the workflow technology as a means for modeling and execution of scientific experiments. Despite being a very powerful paradigm workflows still lack support for trial-and-error modeling, as well as flexibility mechanisms that enable the ad hoc repetition of experiment logic to enable, for example, the convergence of results or to handle errors. In this respect, in our work on enabling multi-scale/field (multi-*) experiments using choreographies of scientific workflows, we contribute a method comprising all necessary steps to conduct the repetition of choreography logic across all workflow instances participating in a multi-* experiment. To realize the method, we contribute i) a formal model representing choreography models and instances, including the re-execute and iterate operations for choreographies, and based on it ii) algorithms for determining the rewinding points, i.e., the activity instances where the rewinding has to stop and iii) enable the actual rewinding to a previous execution state and repetition of the choreography. We present the implementation of our approach in a message-based, service-oriented system that allows scientists to model, control, and execute scientific choreographies as well as perform the rewinding and repeating of choreography logic. We also provide an evaluation of the performance of our approach.

Fully automated provisioning and deployment of applications is one of the most essential prerequisites to make use of the benefits of Cloud computing in order to reduce the costs for managing applications. A huge variety of approaches, tools, and providers are available to automate the involved processes. The DevOps community, for instance, provides tooling and reusable artifacts to implement deployment automation in an application-oriented manner. Platform-as-a-Service frameworks are available for the same purpose. In this work we systematically classify and characterize available deployment approaches independently from the underlying technology used. For motivation and evaluation purposes, we choose Web applications with different technology stacks and analyze their specific deployment requirements. Afterwards, we provision these applications using each of the identified types of deployment approaches in the Cloud to perform qualitative and quantitative measurements. Finally, we discuss the evaluation results and derive recommendations to decide which deployment approach to use based on the deployment requirements of an application. Our results show that deployment approaches can also be efficiently combined if there is no `best fit' for a particular application.

Nowadays, cloud providers offer a broad catalog of services for migrating and distributing applications in the cloud. However, the existence of a wide spectrum of cloud services has become a challenge for deciding where to host applications, as these vary in performance and cost. This work addresses such a challenge, and provides a utility-based decision support model and method that evaluates and ranks during design time potential application distributions spanned among heterogeneous cloud services. The utility model is evaluated using the MediaWiki (Wikipedia) application, and shows an improved efficiency for selecting cloud services in comparison to other decision making approaches.

Tens of thousands of web applications are written in Flask, a Python-based web framework. Despite a rich ecosystem of extensions, there is none that supports the developer in gaining insight into the evolving performance of their service. In this paper, we introduce Flask Dashboard, a library that addresses this problem. We present the ease with which the library can be integrated in an already existing web application, discuss some of the visualization perspectives that the library provides and point to some future challenges for similar libraries.

Utility, defined as the perceived satisfaction with a service, provides the ideal means for decision making on the level of individual entities and collectives participating in a large-scale dynamic system.Previous works have already introduced the concept into the area of collective adaptive systems, and have discussed what is the necessary infrastructure to support the realization of the involved theoretical concepts into actual decision making.In this work we focus on two aspects.First, we provide a concrete utility model for a case study that is part of a larger research project.Second, we incorporate this model into our implementation of the proposed architecture.More importantly, we design and execute an experiment that aims to empirically evaluate the use of utility for decision making by comparing it against simpler decision making mechanisms.

The increase of available Cloud services and providers has contributed to accelerate the development and has broaden the possibilities for building and provisioning Cloud applications in heterogeneous Cloud environments. The necessity for satisfying business and operational requirements in an agile and rapid manner has created the need for adapting traditional methods and tooling support for building and provisioning Cloud applications. Focusing on the application's performance and its evolution, we observe a lack of support for specifying, capturing, analyzing, and reasoning on the impact of using different Cloud services and configurations. This paper bridges such a gap by proposing the conceptual and tooling support to enhance Cloud application topology models to capture and analyze the evolution of the application's performance. The tooling support is built upon an existing modeling environment, which is subsequently evaluated using the MediaWiki (Wikipedia) application and its realistic workload.

The use of process fragments to leverage reuse of process models is well established in the literature. Process fragments are manually or semi-automatically extracted and mainly focus on the textual or behavioural semantics of the process models that they are extracted from. However, in many use cases we also need to use these fragments to derive synthetic process models that satisfy specific structural properties. In order to tackle this challenge we propose a method for automatically generating synthetic, representative, executable process models expressed in Business Process Model and Notation 2.0 (BPMN 2.0) with respect to specific user-defined structural criteria. Our method identifies, selects, and combines recurring sub-structures discovered in a collection of thousands of real world process models. The recurring sub-structures are seen as an extended type of process fragments. For our method we have developed a proof-of-concept prototype and for this we discuss the experimental results obtained from its evaluation.

The emergence and consolidation of container-based virtualization techniques has simplified and accelerated the development, provisioning, and deployment of applications for the Cloud. When considering the case of composite service-based applications that rely on service middleware solutions for their operation, container-based virtualization offers the opportunity for rapid and efficient building and deployment of lightweight, optimally configured middleware instances. As such, it provides an ideal tool for the purposes of cloudifying existing middleware solutions and offering them as part of larger PaaS offerings. As part of this effort, our investigation focuses on leveraging and evaluating a container-based virtualization environment towards enabling the assembly, provisioning, and execution of dynamically tailored instances to satisfy service middleware communication requirements of specific applications. For these purposes we scope the discussion on one particular type of messaging middleware for composite service applications, the Enterprise Service Bus (ESB) technology.

The success of the Cloud computing paradigm, together with the increase of Cloud providers and optimized Infrastructure-as-a-Service (IaaS) offerings have contributed to a raise in the number of research and industry communities that are strong supporters of migrating and running their applications in the Cloud. Focusing on eScience simulation-based applications, scientific workflows have been widely adopted in the last years, and the scientific workflow management systems have become strong candidates for being migrated to the Cloud. In this research work we aim at empirically evaluating multiple Cloud providers and their corresponding optimized and nonoptimized IaaS offerings with respect to their offered performance, and its impact on the incurred monetary costs when migrating and executing a workflow-based simulation environment. The experiments show significant performance improvements and reduced monetary costs when executing the simulation environment in off-premise Clouds.

DevOps is an emerging paradigm to actively foster the collaboration between system developers and operations in order to enable efficient end-to-end automation of software deployment and management processes. DevOps is typically combined with Cloud computing, which enables rapid, on-demand provisioning of underlying resources such as virtual servers, storage, or database instances using APIs in a self-service manner. Today, an ever-growing amount of DevOps tools, reusable artifacts such as scripts, and Cloud services are available to implement DevOps automation. Thus, informed decision making on the appropriate approach (es) for the needs of an application is hard. In this work we present a collaborative and holistic approach to capture DevOps knowledge in a knowledgebase. Beside the ability to capture expert knowledge and utilize crowd sourcing approaches, we implemented a crawling framework to automatically discover and capture DevOps knowledge. Moreover, we show how this knowledge is utilized to deploy and operate Cloud applications.

Reuse of service orchestrations or service compositions is extensively studied in the literature of process modeling. Sub-processes, process templates, process variants, and process reference models are employed as reusable elements for these purposes. The concept of process fragments has been previously introduced in order to capture parts of a process model and store them for later reuse. However, similar efforts on facilitating the reuse of processes that cross the boundaries of organizations expressed as service choreographies are not available yet. In this paper, we introduce the concept of choreography fragments as reusable elements for service choreography modeling. Choreography fragments can be extracted from choreography models, adapted, stored, and later inserted into new models. Based on a formal model for choreography fragments, we define methods and algorithms for the extraction and insertion of fragments from and into service choreographies. We then discuss an experimental and proof-of-concept evaluation of our proposal.

The wide adoption of the cloud computing paradigm by many different domains has increased both the number and type of available offerings as a service, e.g. Database-as-a-service (DBaaS) or Platform-as-a-Service (PaaS), and with them the possibilities to deploy and operate an application partially or completely in the cloud. The need for providing design support to application developers in this environment is the focus of this work. Toward this goal, in the following we first scope the discussion on the persistence layer of applications and we investigate the effect of different deployment scenarios on the performance of the application over time. Based on the results of this analyses we then propose an application (re-)distribution design support process, which we evaluate empirically by means of a well-known application. The results of this evaluation both highlight the strengths of our proposal, and at the same time, provide a clear path for the following steps in our work.

Application designers are faced with a series of design decisions when considering the number of available cloud services that can be used nowadays to host one or more parts of the application. Each one of these services comes with a different pricing model, which results in additional complexity when trying to identify the most cost-efficient solution. Toward this goal, in this work we present our proposal for a design support process that covers both the migration of existing applications and the (re)design of new ones specifically for the cloud. Furthermore, we also discuss an architecture for the realization of this process, as well as our implementation of this architecture.

The Cloud computing paradigm emerged by establishing new resources provisioning and consumption models. Together with the improvement of resource management techniques, these models have contributed to an increase in the number of application developers that are strong supporters of partially or completely migrating their application to a highly scalable and pay-per-use infrastructure. In this paper we derive a set of functional and non-functional requirements and propose a process-based approach to support the optimal distribution of an application in the Cloud in order to handle fluctuating over time workloads. Using the TPC-H workload as the basis, and by means of empirical workload analysis and characterization, we evaluate the application persistence layer's performance under different deployment scenarios using generated workloads with particular behavior characteristics.

Large-scale systems comprising of multiple heterogeneous entities are directly influenced by the interactions of their participating entities.Such entities, both physical and virtual, attempt to satisfy their objectives by dynamically collaborating with each other, and thus forming collective adaptive systems.These systems are subject to the dynamicity of the entities' objectives, and to changes to the environment.In this work we focus on the latter, i.e. on providing the means for entities in such systems to model, monitor and evaluate their perceived utility by participating in the system.This allows for them to make informed decisions about their interactions with other entities in the system.For this purpose we propose a utility-based approach for decision making, as well as an architecture that allows for the support of this approach.

Service orientation has significantly facilitated the development of complex distributed systems spanning multiple organizations.However, different application areas approach such systems in domain-specific ways, focusing only on particular aspects relevant for their application types.As a result, we observe a very fragmented landscape of service-oriented systems, which does not enable collaboration across organizations.To address this concern, in this work we introduce the notion of Collaborative, Dynamic and Complex (CDC) systems and position them with respect to existing technologies.In addition, we present how CDC systems are modeled and the steps to provision and execute them.Furthermore, we contribute an architecture and prototypical implementation, which we evaluate by means of a case study in a Cloud-enabled context-aware pervasive application.

In the last years Cloud computing has become popular among IT organizations aiming to reduce their operational costs. Applications can be designed to run in the Cloud, or can be partially or completely migrated to it. Migrating the data layer of an application to the Cloud, however, implies that existing applications might need to be adapted in order to access their migrated to the Cloud databases. In this work we examine how we can use an existing ESB to enable transparent access to the relational data store running either in the Cloud or on-premise. The goal of our approach is to minimize the effort required to adapt the application. In particular, we discuss the requirements and prototype realization of a Cloud aware data access layer for transparent data access, using an existing open source and multi-tenant aware ESB as the basis. We then evaluate the performance of our proposed solution by considering different Cloud providers and using example data from an existing benchmark as application workload.

Nowadays different Cloud services enable enterprises to migrate applications to the Cloud. An application can be partially migrated by replacing some of its components with Cloud services, or by migrating one or multiple of its layers to the Cloud. As a result, accessing application data stored off-premise requires mechanisms to mitigate the negative impact on Quality of Service (QoS), e.g. due to network latency. In this work, we propose and realize an approach for transparently accessing data migrated to the Cloud using a multi-tenant open source Enterprise Service Bus (ESB) as the basis. Furthermore, we enhance the ESB with QoS awareness by integrating it with an open source caching solution. For evaluation purposes we generate a representative application workload using data from the TPC-H benchmark. Based on this workload, we then evaluate the optimal caching strategy among multiple eviction algorithms when accessing relational databases located at different Cloud providers.

Fully automated provisioning and deployment in order to reduce the costs for managing applications is one of the most essential requirements to make use of the benefits of Cloud computing. Several approaches and tools are available to automate the involved processes. The DevOps community, for example, provides tooling and artifacts to realize deployment automation on Infrastructure as a Service level in a mostly application-oriented manner. Platform as a Service frameworks are also available for the same purpose. In this paper we categorize and characterize available deployment approaches independently from the underlying technology used. For this purpose, we choose Web applications with different technology stacks and analyze their specific deployment requirements. Afterwards, we provision these applications using each of the identified types of deployment approaches in the Cloud. Finally, we discuss the evaluation results and derive recommendations which deployment approach to use based on the deployment requirements of an application.

The motivation for this work is the necessity to be able to select an appropriate Cloud service provider offering for the migration of existing applications, based on cost minimization. While service providers offer pricing information publicly, and online tools allow for the calculation of cost for various Cloud offerings, the selection of which offering fits better the application requirements is left to application developers. For this purpose, this work proposes a migration decision support system that incorporates both offering matching and cost calculation, combining features from various approaches in the State of the Art. The proposed approach is then evaluated against existing tools.

The Platform as a Service (PaaS) model promotes the development and deployment of applications on top of middleware solutions offered by the provider. Deploying an application in this model entails both the deployment of the application on top of the platform, but potentially also the deployment of the middleware components required by the application. Existing works enable the abstraction from the underlying infrastructure and allow for the creation of generic deployment plans for middleware components that automate the deployment of applications. In this paper we propose a middleware-oriented deployment approach that defines how the deployment of middleware components can be defined in a manner that allows to offer them as PaaS building blocks, and enable the automatic deployment of application components on them. We also present an evaluation of our proposal, together with the lessons learned during this activity.

Migrating an existing application to the Cloud is a complex and multi-dimensional problem requiring in many cases adapting the application in significant ways. Looking specifically into the database layer of the application, i.e. the aspect providing data persistence and manipulation capabilities, this involves dealing with differences in the granularity of interactions, refactoring of the application to cope with remote data sources, and addressing data confidentiality concerns. Toward this goal, in this work we present an application migration methodology which incorporates these aspects, and a decision support, application refactoring and data migration tool that assists application developers in realizing this methodology. For purposes of evaluating our proposal we present the results of a case study conducted in the context of an eScience project.

Cloud services allow for hosting applications partially or completely in the Cloud by migrating their components and data. Especially with respect to data migration, a series of functional and non-functional challenges like data confidentiality arise when considering private and public Cloud data stores. In this paper we identify some of these challenges and propose a set of reusable solutions focusing on the non-functional aspects, organized together as a set of Cloud Data Patterns.

Multi-tenancy is an essential property of Cloud computing. It helps service providers to maximize resource utilization and reduce servicing costs per customer. It is therefore important for key components of the contemporary enterprise environment like the Enterprise Service Bus (ESB) to support and enable multi-tenancy. For this purpose, in this work we investigate the requirements for multi-tenant ESB solutions, propose an implementation-agnostic ESB architecture that addresses these requirements, and discuss our proof-of-concept realization of this architecture.

Web services privacy issues have been attracting more and more attention in the past years. Since the number of Web services-based business applications is increasing, the demands for privacy enhancing technologies for Web services will also be increasing in the future. In this paper, we investigate an extension of business protocols, i.e. the specification of which message exchange sequences are supported by the web service, in order to accommodate privacy aspects and time-related properties. For this purpose we introduce the notion of Timed Privacy-aware Business Protocols (TPBPs). We also discuss TPBP properties can be checked and we describe their verification process.

In an environment of constant change and variation driven by competition and innovation, a software service can rarely remain stable. Being able to manage and control the evolution of services is therefore an important goal for the Service-Oriented paradigm. This work extends existing and widely adopted theories from software engineering, programming languages, service-oriented computing, and other related fields to provide the fundamental ingredients required to guarantee that spurious results and inconsistencies that may occur due to uncontrolled service changes are avoided. The paper provides a unifying theoretical framework for controlling the evolution of services that deals with structural, behavioral, and QoS level-induced service changes in a type-safe manner, ensuring correct versioning transitions so that previous clients can use a versioned service in a consistent manner.

Service retrieval holds a central role during the development of Web services and Service-Based Applications (SBAs). The higher the number of available services, the more complex it becomes to locate the service closer to the developer needs. The complexity increases further with the number of available service versions that could also be suitable for this purpose. Existing approaches on service retrieval use a similarity measure between service interfaces to identify potentially relevant services. In this work we focus on introducing information about the compatibility of services while calculating their similarity as the means for providing more suitable results. For this purpose we update and extend an existing Web services matchmaker called UDDI Registry by Example (URBE).