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Abstract-We present the ASKALON environment whose goal is to simplify the development and execution of workflow applications on the Grid. ASKALON is centered around a set of high-level services for transparent and effective Grid access, including a Scheduler for optimized mapping of workflows onto the Grid, an Enactment Engine for reliable application execution, a Resource Manager covering both computers and application components, and a Performance Prediction service based on training phase and statistical methods. A sophisticated XMLbased programming interface that shields the user from the Grid middleware details allows the high-level composition of workflow applications. ASKALON is used to develop and port scientific applications as workflows in the Austrian Grid project. We present experimental results using two real-world scientific applications to demonstrate the effectiveness of our approach.

Cloud computing is ever stronger converging with the Internet of Things (IoT) offering novel techniques for IoT infrastructure virtualization and its management on the cloud. However, system designers and operations managers face numerous challenges to realize IoT cloud systems in practice, mainly due to the complexity involved with provisioning large-scale IoT cloud systems and diversity of their requirements in terms of IoT resources consumption, customization of IoT capabilities and runtime governance. In this paper, we introduce the concept of software-defined IoT units -a novel approach to IoT cloud computing that encapsulates fine-grained IoT resources and IoT capabilities in well-defined APIs in order to provide a unified view on accessing, configuring and operating IoT cloud systems. Our software-defined IoT units are the fundamental building blocks of software-defined IoT cloud systems. We present our framework for dynamic, on-demand provisioning and deploying such software-defined IoT cloud systems. By automating provisioning processes and supporting managed configuration models, our framework simplifies provisioning and enables flexible runtime customizations of software-defined IoT cloud systems. We demonstrate its advantages on a real-world IoT cloud system for managing electric fleet vehicles.

Abstract. High quality context information plays a vital role in adapting a system to the rapidly changing situations. However, the diversity of the sources of context information and the characteristics of the computing devices strongly impact the quality of context information in pervasive computing environments. Quality of Context parameters can be used to characterize the quality of context information from different aspects. In this paper, we quantify the Quality of Context parameters to present them in a suitable form for use with the applications in pervasive environments. We also present a mechanism to tailor the Quality of Context parameters according to the needs of an application and then evaluate these parameters. Enrichment of context information with Quality of Context parameters enhances the capabilities of context-aware systems to effectively use the context information to adapt to the emerging situations in pervasive computing environments.

This invited article explores how Internet of Things (IoT) cloud systems could provide a coherent software layer for continuous deployment, provisioning, and execution of applications for various domains.

Abstract. Fine-grained elasticity control of cloud services has to deal with multiple elasticity perspectives (quality, cost, and resources). We propose a cloud services elasticity control mechanism that considers the service structure for controlling the cloud service elasticity at multiple levels, by firstly defining an abstract composition model for cloud services and enabling multi-level elasticity control. Secondly, we define mechanisms for solving conflicting elasticity requirements and generating action plans for elasticity control. Using the defined concepts and mechanisms we develop a runtime system supporting multiple levels of elasticity control and validate the resulted prototype through experiments.

Providing data as a service has not only fostered the access to data from anywhere at anytime but also reduced the cost of investment. However, data is often associated with various concerns that must be explicitly described and modeled in order to ensure that the data consumer can find and select relevant data services as well as utilize the data in the right way. In particular, the use of data is bound to various rules imposed by data owners and regulators. Although, technically Web services and database technologies allow us to quickly expose data sources as Web services, until now, research has not been focused on the description of data service concerns, thus hindering the discovery, selection and utilization of data services. In this paper, we analyze major concerns for data as a service, model these concerns, and discuss how they can be used to improve the search and utilization of data services.