2015
DOI: 10.1007/978-3-662-48616-0_3
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Analysis of Timing Constraints in Heterogeneous Middleware Interactions

Abstract: With the emergence of Future Internet applications that connect web services, sensor-actuator networks and service feeds, scalability and heterogeneity support of interaction paradigms are of critical importance. Heterogeneous interactions can be abstractly represented by client-service, publish-subscribe and tuple space middleware connectors that are interconnected via bridging mechanisms providing interoperability among the services. In this paper, we make use of the eXtensible Service Bus (XSB), proposed in… Show more

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Cited by 5 publications
(11 citation statements)
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“…Communication protocols underlying distributed systems may be classified along the following coupling dimensions [15], [16], [20]:…”
Section: B Computer-mediated Social Communication: a Technical Perspmentioning
confidence: 99%
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“…Communication protocols underlying distributed systems may be classified along the following coupling dimensions [15], [16], [20]:…”
Section: B Computer-mediated Social Communication: a Technical Perspmentioning
confidence: 99%
“…This is illustrated in We then need to study the extent to which different types of social interaction may be reconciled and when it is appropriate to synthesize the corresponding communication protocol adaptation. To do so, we build upon the eXtensible Service Bus (XSB) [15], [16] that is an approach to reconcile the middleware protocols run by networked systems across the various coupling dimensions (i.e., space, time, synchronization). This leads us to introduce the Social communication bus paradigm.…”
Section: Communication Service Interoperabilitymentioning
confidence: 99%
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“…Async PS interactions will be supported by using the RabbitMQ 10 protocol (commonly used for mobile applications). Subsequently, our previous work on the analysis of timing constraints in heterogeneous middleware interactions [17] will enable our middleware to deal with intermittent network connectivity, and distinguish among the supported interactions to improve the message delays vs. delivery success rates. Moreover, estimated message delays derived using our queueing analysis will enable our middleware to apply appropriate timing parameters.…”
Section: Implementation Detailsmentioning
confidence: 99%