Service composition is a popular approach for building software applications from several individual services. Using imperative workflow technologies, service compositions can be specified as workflow models comprising activities that are implemented, e.g., by service calls or scripts. While scripts are typically included in the workflow model itself and can be executed directly by the workflow engine, the required services must be deployed in a separate step. Moreover, to enable their invocation, an additional step is required to configure the workflow model regarding the endpoints of the deployed services, i.e., IP-address, port, etc. However, a manual deployment of services and configuration of the workflow model are complex, time-consuming, and error-prone tasks. In this paper, we present an approach that enables defining service compositions in a self-contained manner using imperative workflow technology. For this, the workflow models can be packaged with all necessary deployment models and software artifacts that implement the required services. As a result, the service deployment in the target environment where the workflow is executed as well as the configuration of the workflow with the endpoint information of the services can be automated completely. We validate the technical feasibility of our approach by a prototypical implementation based on the TOSCA standard and OpenTOSCA.
The continuous growth of the Internet of Things together with the complexity of modern information systems results in several challenges for modeling, provisioning, executing, and maintaining systems that are capable of adapting themselves to changing situations in dynamic environments. The properties of the workflow technology, such as its recovery features, makes this technology suitable to be leveraged in such environments. However, the realization of situation-aware mechanisms that dynamically adapt process executions to changing situations is not trivial and error prone, since workflow modelers cannot reflect all possibly occurring situations in complex environments in their workflow models. In this paper, we present a method and concepts to enable modelers to create traditional, situation-independent workflow models that are automatically transformed into situation-aware workflow models that cope with dynamic contextual situations. Our work builds upon the usage of workflow fragments, which are dynamically selected during runtime to cope with prevailing situations retrieved from low-level context sensor data. We validate the practical feasibility of our work by a prototypical implementation of a Situation-aware Workflow Management System (SaWMS) that supports the presented concepts.
Abstract. The Internet of Things benefits from an increasing number of interconnected technical devices. This has led to the existence of so-called smart environments, which encompass one or more devices sensing, acting, and automatically performing different tasks to enable their self-organization. Smart environments are divided into two parts: the physical environment and its digital representation, oftentimes referred to as digital twin. However, the automated binding and monitoring of devices of smart environments are still major issues. In this article we present a method and system architecture to cope with these challenges by enabling (i) easy modeling of sensors, actuators, devices, and their attributes, (ii) dynamic device binding based on their type, (iii) the access to devices using different paradigms, and (iv) the monitoring of smart environments in regard to failures or changes. We furthermore provide a prototypical implementation of the introduced approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.