Blockchains have become increasingly important in recent years and have expanded their applicability to many domains beyond finance and cryptocurrencies. This adoption has particularly increased with the introduction of smart contracts, which are immutable, user-defined programs directly deployed on blockchain networks. However, many scenarios require business transactions to simultaneously access smart contracts on multiple, possibly heterogeneous blockchain networks while ensuring the atomicity and isolation of these transactions, which is not natively supported by current blockchain systems. Therefore, in this work, we introduce the Transactional Cross-Chain Smart Contract Invocation (TCCSCI) approach, which supports such distributed business transactions while ensuring their global atomicity and serializability. The approach introduces the concept of Resource Manager Smart Contracts (RMSCs), and 2PC for Blockchains (2PC4BC), a client-driven Atomic Commit Protocol (ACP) specialized for blockchain-based distributed transactions. We validate our approach using a prototypical implementation, evaluate its introduced overhead, and prove its correctness.

Abstract Function‐as‐a‐Service (FaaS) is a cloud service model enabling to implement serverless applications for a variety of use cases. These range from scheduled calls of single functions to complex function orchestrations executed using orchestration services such as AWS step functions. However, since the available function orchestration technologies vary in functionalities, supported modeling languages, and APIs, modeling such function orchestrations and their deployment require significant technology‐specific expertise. Moreover, the resulting models are typically not portable due to provider‐ and technology‐specific details, and major efforts are required when exchanging an orchestrator or provider due to such lock‐ins. To tackle this issue, we introduce a vendor‐ and technology‐agnostic method for the modeling and deployment of serverless function orchestrations, which relies on the business process model and notation (BPMN) and topology and orchestration specification for cloud applications (TOSCA) standards for modeling function orchestrations and their deployment, respectively. We also present a toolchain for modeling serverless function orchestrations in BPMN, generating proprietary models supported by different function orchestration technologies from BPMN models, specifying their actual deployment in TOSCA, and then enacting such deployment. Finally, we illustrate a case study applying our method and toolchain in practice.

The diversity of available cloud service models yields multiple hosting variants for application components. Moreover, the overall trend of reducing control over the infrastructure and scaling configuration makes it non-trivial to decide which hosting variant suits more a certain software component. In this work, we introduce a spectrum of component hosting patterns that covers various combinations of management responsibilities related to (i) the deployment stack required by a given component as well as (ii) required infrastructure resources and component’s scaling rules. We validate the presented patterns by identifying and showing at least three real world occurrences of each pattern following the well-known Rule of Three.

Function-as-a-Service (FaaS) is a cloud service model enabling developers to offload event-driven executable snippets of code. The execution and management of such functions becomes a FaaS provider’s responsibility, therein included their on-demand provisioning and automatic scaling. Key enablers for this cloud service model are FaaS platforms, e.g., AWS Lambda, Microsoft Azure Functions, or OpenFaaS. At the same time, the choice of the most appropriate FaaS platform for deploying and running a serverless application is not trivial, as various organizational and technical aspects have to be taken into account. In this work, we present (i) a FaaS platform classification framework derived using a multivocal review and (ii) a technology review of the ten most prominent FaaS platforms, based on the proposed classification framework. We also present a FaaS platform selection support system, called FaaStener, which can help researchers and practitioners to choose the FaaS platform most suited for their requirements.

\n The automation of application deployment is critical because\n manually deploying applications is time-consuming, tedious, and\n error-prone. Several deployment automation technologies have been\n developed in recent years employing tool-specific deployment\n modeling languages. At the same time, the OASIS standard Topology\n Orchestration Specification for Cloud Applications (TOSCA) emerged\n as a means for describing cloud applications, i. e., their\n components and relationships, in a vendor-agnostic fashion. Despite\n TOSCA is widely used in research, it is not supported by the\n production-ready deployment automation technologies daily used by\n practitioners working with cloud-native applications, hence\n resulting in a gap between the state-of-the-art in research and\n state-of-practice in the industry. To help bridging this gap, we\n leverage the recently introduced Essential Deployment Metamodel\n (EDMM) and identify TOSCA Light, an EDMM-compliant subset of TOSCA,\n to enact the transformation from TOSCA to the vast majority of\n deployment automation technology-specific models used by todayâs\n software industry. Further, we present an end-to-end TOSCA Light\n modeling and transformation workflow and show a prototypical\n implementation to validate our approach.\n

\n The term serverless is often used to describe cloud applications\n that comprise components managed by third parties. Like any other\n cloud application, serverless applications are often tightly-coupled\n with providers, their features, models, and APIs. As a result, when\n their portability to another provider has to be assessed,\n application owners must deal with identification of heterogeneous\n lock-in issues and provider-specific technical details.\n Unfortunately, this process is tedious, error-prone, and requires\n significant technical expertise in the domains of serverless and\n cloud computing. In this work, we introduce SEAPORT, a method for\n automatically assessing the portability of serverless applications\n with respect to a chosen target provider or platform. The method\n introduces (i) a canonical serverless application model, and (ii)\n the concepts for portability assessment involving classification and\n components similarity calculation together with the static code\n analysis. The method aims to be compatible with existing migration\n concepts to allow using it as a complementary part for serverless\n use cases. We present an architecture of a decision support system\n supporting automated assessment of the given application model with\n respect to the target provider. To validate the technical\n feasibility of the method, we implement the system prototypically.\n

\n Complex applications are typically composed of multiple components.\n In order to install these components all their dependencies need to\n be satisfied. Typically these dependencies are resolved, downloaded,\n and installed during the deployment time and in the target\n environment, e.g., using package manager of the operating system.\n However, under some circumstances this approach is not applicable,\n e.g., if the access to the Internet is limited or non-existing at\n all. For instance, Industry 4.0 environments often have no Internet\n access for security reasons. Thus, in these cases, deployment\n packages without external dependencies are required that already\n contain everything required to deploy the software. In this paper,\n we present an approach enabling the transformation of\n non-self-contained deployment packages into self-contained\n deployment packages. Furthermore, we present a method for developing\n self-contained deployment packages systematically. The practical\n feasibility is validated by a prototypical implementation following\n our proposed system architecture. Moreover, our prototype is\n evaluated by provisioning a LAMP stack using the open-source\n ecosystem OpenTOSCA.\n

Microservice-based architectures (MSAs) gained momentum in industrial and research communities since finer-grained and more independent components foster reuse and reduce time to market. However, to come from the design of MSAs to running applications, substantial knowledge and technology-specific expertise in the deployment and integration of microservices is needed. In this paper, we propose a model-driven and pattern-based approach for composing microservices, which facilitates the transition from architectural models to running deployments. Using a unified modelling for MSAs, including both their integration based on Enterprise Integration Patterns (EIPs) and deployment aspects, our approach enables automatically generating the artefacts for deploying microservice compositions. This helps abstracting away the underlying infrastructure including container orchestration platforms and middleware layer for service integration. To validate the feasibility of our approach, we illustrate its prototypical implementation, with Kubernetes used as container orchestration system and OpenFaaS used for managing integration logic, and we present a case study.

Blockchains are distributed systems that facilitate the interaction of autonomous entities with limited mutual trust. Many of them support transactional applications known as smart contracts, which access and modify the shared world state. Permissionless blockchains are completely decentralized and do not require mutual trust between interacting peers, but at the expense of having low performance and limited data confidentiality capabilities. On the other hand, permissioned blockchains solve these issues, but sacrifice complete decentralization and involve more trust assumptions. Therefore, there is no single blockchain system suitable for all use-cases. However, this becomes a serious integration challenge for enterprises that need to interact with multiple permissioned and permissionless blockchains in the same context. To facilitate this, we propose an approach that enables composing smart contract functions of various permissioned and permissionless blockchain systems by providing the ability to invoke them directly from business process models using a new task type. To keep this task blockchain-agnostic, we designed a generic technique to identify smart contract functions, as well as a generic metric to describe the degree-of-confidence in the finality of blockchain transactions. Thereby, the proposed approach extends our previous work, BlockME, which provides business modeling extensions only suitable for interacting with permissionless blockchains. To validate the practical feasibility of our approach, we provide a detailed system architecture and a prototypical implementation supporting multiple blockchains.

Function-as-a-Service (FaaS) is an emerging cloud service model that enables composing applications using arbitrary, small, and event-driven code snippets managed by cloud providers and that can be scaled to zero. The scalability properties of FaaS look attractive for handling rare or unexpected high loads that affect only particular functionalities of the application. However, deciding on the component granularity upfront or reengineering the architecture of an entire application for rare workloads is often a very difficult challenge or even infeasible. In this work, we introduce a method that prepares annotated functionalities for handling rare workloads by automatically extracting them from the source code of the application and additionally deploying them as FaaS functions, while keeping the original application's functionalities and architecture unchanged. In this way, the benefits of FaaS can be leveraged without the need to reengineer the application only for rare cases. We validate our method by means of a prototype, evaluate its feasibility in a set of experiments, and discuss limitations and future work.

The importance of data is steadily increasing in the domain of business process management due to recent advances in data science, IoT, and Big Data. To reflect this paradigm shift towards data-awareness in service choreographies, we introduced the notion of data-aware choreographies based on concepts for Transparent Data Exchange (TraDE) in our previous works. The goal is to simplify the modeling of business-relevant data and its exchange in choreography models while increasing their run time flexibility. To further improve and simplify the modeling of data-related aspects in service choreographies, in this paper, we focus on the extension of our TraDE concepts to support the modeling of data transformations in service choreographies. Such data transformation capabilities are of dire need to mediate between different data formats, structures and representations of the collaborating participants within service choreographies. Therefore, the paper presents a modeling extension as means for specifying and executing heterogeneous data transformations in service choreographies based on our TraDE concepts.