An application for analysis of structures under static loading is ported to the cloud using the mOSAIC portable platform-as-a-service. The new cloud application benefits from Web availability, elasticity, and fault to...
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An application for analysis of structures under static loading is ported to the cloud using the mOSAIC portable platform-as-a-service. The new cloud application benefits from Web availability, elasticity, and fault tolerance, while being independent from the infrastructure-as-a-service providers. This achievement paves the way for porting a range of engineering applications to multiple clouds.
Service compositions suffer changes in their partner services. Even if the composition does not change, its behavior may evolve over time and become incorrect. Such changes cannot be fully foreseen through prerelease ...
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Service compositions suffer changes in their partner services. Even if the composition does not change, its behavior may evolve over time and become incorrect. Such changes cannot be fully foreseen through prerelease validation, but impose a shift in the quality assessment activities. Provided functionality and quality of service must be continuously probed while the application executes, and the application itself must be able to take corrective actions to preserve its dependability and robustness. We propose the idea of self-supervising BPEL processes, that is, special-purpose compositions that assess their behavior and react through user-defined rules. Supervision consists of monitoring and recovery. The former checks the system's execution to see whether everything is proceeding as planned, while the latter attempts to fix any anomalies. The paper introduces two languages for defining monitoring and recovery and explains how to use them to enrich BPEL processes with self-supervision capabilities. Supervision is treated as a crosscutting concern that is only blended at runtime, allowing different stakeholders to adopt different strategies with no impact on the actual business logic. The paper also presents a supervision-aware runtime framework for executing the enriched processes, and briefly discusses the results of in-lab experiments and of a first evaluation with industrial partners.
We propose a design methodology, Service-Oriented Design with Aspects (SODA), for service-oriented systems to address the need to continually upgrade and evolve services while maintaining various versions. Our approac...
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We propose a design methodology, Service-Oriented Design with Aspects (SODA), for service-oriented systems to address the need to continually upgrade and evolve services while maintaining various versions. Our approach treats aspects as first-class design elements and consistently applies the concept of aspect to all phases of design and evaluation. At the early design stages, crosscutting concerns are first separated out as aspects, and then, services are composed by weaving the different design elements together. The behavior of aspects and services is represented as basic Petri Nets and we present rules for weaving together Petri Nets so as to obtain behavior of the integrated system (with aspects crosscutting services). Even at the evaluation stages, performance and resource data are separated out as aspects to be woven in to the design so as to enable advanced analysis using Petri Net tools. A small order service example is used to illustrate our approach.
The rapid adoption of service-oriented architecture (SOA) in many large-scale distributed applications requires the development of adaptive service-basedsoftware systems (ASBS) with the capability of monitoring the c...
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The rapid adoption of service-oriented architecture (SOA) in many large-scale distributed applications requires the development of adaptive service-basedsoftware systems (ASBS) with the capability of monitoring the changing system status, analyzing, and controlling tradeoffs among various quality-of-service (QoS) aspects, and adapting service configurations to satisfy multiple QoS requirements simultaneously. In this paper, our results toward the development of adaptive service-basedsoftware systems are presented. The formulation of Activity-State-QoS (ASQ) models and how to use the data from controlled experiments to establish ASQ models for capturing the cause-effect dynamics among service activities, system resource states, and QoS in service-based systems are presented. Then, QoS monitoring modules based on ASQ models and SOA-compliant simulation models are developed to support the validation of the ASBS design. The main idea for developing QoS adaptation modules based on ASQ models is discussed. An experiment based on a voice communication service is used to illustrate our results.
The High-Level Architecture (HLA) standard developed by the Department of Defense in the United States is a key technology to perform distributed simulation. Inside the HLA framework, many different simulators (termed...
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The High-Level Architecture (HLA) standard developed by the Department of Defense in the United States is a key technology to perform distributed simulation. Inside the HLA framework, many different simulators (termed federates) may be interconnected to create a single more complex simulator (federation). Data Distribution Management (DDM) is an optional subset of services that controls which federates should receive notification of state modifications made by other federates. A simple DDM implementation will usually generate much more traffic than needed, whereas a complex one might introduce too much overhead. In this work, we describe an approach to DDM that delegates a portion of the DDM computation to a processor on the network card in order to provide more CPU time for other federate and Runtime Infrastructure (RTI) computations while still being able to exploit the benefits of a complex DDM implementation to reduce the amount of information exchange.
Scientific workflows are a topic of great interest in the Grid community that sees in the workflow model an attractive paradigm for programming distributed wide-area Grid infrastructures. Traditionally, the Grid workf...
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Scientific workflows are a topic of great interest in the Grid community that sees in the workflow model an attractive paradigm for programming distributed wide-area Grid infrastructures. Traditionally, the Grid workflow execution is approached as a pure best effort scheduling problem that maps the activities onto the Grid processors based on appropriate optimization or local matchmaking heuristics such that the overall execution time is minimized. Even though such heuristics often deliver effective results, the execution in dynamic and unpredictable Grid environments is prone to severe performance losses that must be understood for minimizing the completion time or for the efficient use of high-performance resources. In this paper, we propose a new systematic approach to help the scientists and middleware developers understand the most severe sources of performance losses that occur when executing scientific workflows in dynamic Grid environments. We introduce an ideal model for the lowest execution time that can be achieved by a workflow and explain the difference to the real measured Grid execution time based on a hierarchy of performance overheads for Grid computing. We describe how to systematically measure and compute the overheads from individual activities to larger workflow regions and adjust well-known parallel processing metrics to the scope of Grid computing, including speedup and efficiency. We present a distributed online tool for computing and analyzing the performance overheads in real time based on event correlation techniques and introduce several performance contracts as quality-of-service parameters to be enforced during the workflow execution beyond traditional best effort practices. We illustrate our method through postmortem and online performance analysis of two real-world workflow applications executed in the Austrian Grid environment.
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