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SUMMARYPerformance engineering of parallel and distributed applications is a complex task that iterates through various phases, ranging from modeling and prediction, to performance measurement, experiment management, data collection, and bottleneck analysis. There is no evidence so far that all of these phases should/can be integrated into a single monolithic tool. Moreover, the emergence of computational Grids as a common single wide-area platform for high-performance computing raises the idea to provide tools as interacting Grid services that share resources, support interoperability among different users and tools, and, most importantly, provide omnipresent services over the Grid. We have developed the ASKALON tool set to support performance-oriented development of parallel and distributed (Grid) applications. ASKALON comprises four tools, coherently integrated into a service-oriented architecture. SCALEA is a performance instrumentation, measurement, and analysis tool of parallel and distributed applications. ZENTURIO is a general purpose experiment management tool with advanced support for multi-experiment performance analysis and parameter studies. AKSUM provides semi-automatic highlevel performance bottleneck detection through a special-purpose performance property specification language. The PerformanceProphet enables the user to model and predict the performance of parallel applications at the early stages of development. In this paper we describe the overall architecture of the ASKALON tool set and outline the basic functionality of the four constituent tools. The structure of each tool is based on the composition and sharing of remote Grid services, thus enabling tool interoperability. In addition, a data repository allows the tools to share the common application performance and output data that have been derived by the individual tools. A service repository is used to store common portable Grid service implementations. A general-purpose Factory service is employed to create service instances on arbitrary remote Grid sites. Discovering and dynamically binding to existing remote services is achieved through registry services. The ASKALON visualization diagrams support both online and postmortem visualization of performance and output data. We demonstrate the usefulness and effectiveness of ASKALON by applying the tools to real-world applications.

Purpose of this paperThis survey aims at studying and analyzing current techniques and methods for context-aware Web service systems, discussing future trends and proposing further steps on making Web services systems being context-aware.Design/methodology/approach We analyzed and compared existing context-aware Web servicebased systems based on techniques they support, such as context information modeling, context sensing, distribution, security and privacy, and adaptation techniques. Existing systems are also examined in terms of application domains, system type, mobility support, multi-organization support and level of Web services implementation. FindingsSupporting context-aware Web service-based systems is increasing. It is hard to find a truly context-aware Web service-based system that is interoperable and secure, and operates on multiorganizational environments. Various issues, such as distributed context management, context-aware service modeling and engineering, context reasoning and quality of context, security and privacy issues have not been well addressed. Research limitations/implications (if applicable)The number of systems analyzed is limited. Furthermore, the survey is based on published papers. Therefore, up-to-date information and development might not be taken into account.What is original/value of paper Existing surveys do not focus on context-awareness techniques for Web services. This paper helps to understand the state of the art in context-aware techniques for Web services that can be employed in the future of services which is built around, amongst other, mobile devices, Web services, and pervasive environments.A previous version of this paper was published, as an invited paper, in the International Journal of Web Information Systems, 5(

Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information.In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments. IntroductionPervasive environments are characterized by a plethora of computing and communication enabled devices that diffuse themselves in everyday living and become invisible (Weiser, 1991). These devices implicitly sense and provide context, a core task in making a system adaptable, that is far more complicated than explicit input to the system (Gray & Salber, 2001;Mostefaoui et al., 2004). Quality of context information is deteriorated during this process and contrary to general assumption context information can be incomplete, inaccurate, and ambiguous (Dey, 2001;Henricksen et al., 2002). Inadequate quality of context information severely influences the adaptiveness of context-aware applications (Dey, 2001;Chen & Kotz, 2002;Henricksen & Indulska, 2004). Context-aware applications also perform extra effort to cope with uncertainty of context information (Ranganathan et al., 2004). Quality of Context (QoC), a measurable metric that provides information about the quality of context, can help resolving uncertain and conflicting situations about context information. Therefore, context-aware applications can take advantage of QoC if they are provided with usable QoC metrics that are evaluated considering their requirements regarding the collection, processing, and provision of context information. Currently, there is not only a lack of solutions that evaluate QoC metrics and pass them along with context information to context consumers, but also existing definitions of QoC ignore its multi-facetted nature and consider it as an objective term.In this paper we consider both objective and subjective views of QoC and redefine QoC. The objective view of QoC presents quality of context information independent of the requirements of a context consumer while the subjective view of QoC considers quality of context information as https://www.cambridge.org/core/terms. https://doi

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The forthcoming two-satellite GMES Sentinel-1 constellation is expected to render systematic surface soil moisture retrieval at 1 km resolution using C-band SAR data possible for the first time from space. Owing to the constellation's foreseen coverage over the Sentinel-1 Land Masses acquisition region-global approximately every six days, nearly daily over Europe and Canada depending on latitude-in the high spatial and radiometric resolution Interferometric Wide Swath (IW) mode, the Sentinel-1 mission shows high potential for global monitoring of surface soil moisture by means of fully automatic retrieval techniques. This paper presents the potential for providing such a service systematically over Land Masses and in near real time using a change detection approach, concluding that such a service is-subject to the mission operating as foreseen-expected to be technically feasible. The work presented in this paper was carried out as a feasibility study within the framework of the ESA-funded GMES Sentinel-1 Soil Moisture Algorithm Development (S1-SMAD) project.

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