Eventlets: Components for the integration of event streams with SOA

Appel, Stefan; Frischbier, Sebastian; Freudenreich, Tobias; Buchmann, Alejandro

doi:10.1109/soca.2012.6449455

Cited by 13 publications

(14 citation statements)

References 24 publications

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“…SenaaS [8] also focuses on providing sensor management and address the issue of combining event streams and services in the cloud. Although, not specifically tailored for cloud, in [9] the authors also examine similar problem and provide a solution in form of event processing containers. OpenIoT framework [21] enables pay-as-you-go sensing as a service and linking sensory data.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

PatRICIA -- A Novel Programming Model for IoT Applications on Cloud Platforms

Nastić

Sehic

Vögler

et al. 2013

2013 IEEE 6th International Conference on Service-Oriented Computing and Applications

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Abstract-Cloud computing technologies have recently been intensively exploited for the development and management of large-scale IoT systems, due to their capability to integrate diverse types of IoT devices and to support big IoT data analytics in an elastic manner. However, due to the diversity, complexity and scale of IoT systems, the need to handle large volumes of IoT data in a nontrivial manner, and the plethora of domaindependent IoT controls, programming IoT applications on cloud platforms still remains a great challenge. To date, existing work neglects high-level programming models and focuses on lowlevel IoT data and device integration. In this paper, we outline PatRICIA, which aims at providing an end-to-end solution for high-level programming and provisioning of IoT applications on cloud platforms. We present a novel programming model, based on the concept of intent and intent scope. Further, we introduce its runtime for dealing with the complexity, diversity and scale of IoT systems in the cloud. Our programming model defines abstractions to enable easier, efficient and more intuitive development of cloud-scale IoT applications. To illustrate our programming model, we present a case study with real-world applications for controlling and managing electric vehicles.

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Section: Implementation and Evaluationmentioning

confidence: 99%

PatRICIA -- A Novel Programming Model for IoT Applications on Cloud Platforms

Nastić

Sehic

Vögler

et al. 2013

2013 IEEE 6th International Conference on Service-Oriented Computing and Applications

View full text Add to dashboard Cite

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“…Prior work on Eventlets [6] comes close to our work on Rx4DDS.NET. Eventlets provides a container abstraction to encapsulate the complex event processing logic inside a component so that a component-based service oriented architecture can be realized.…”

Section: Related Workmentioning

confidence: 68%

Reactive stream processing for data-centric publish/subscribe

Khare

Gokhale

et al. 2015

Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems

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The Internet of Things (IoT) paradigm has given rise to a new class of applications wherein complex data analytics must be performed in real-time on large volumes of fastmoving and heterogeneous sensor-generated data. Such data streams are often unbounded and must be processed in a distributed and parallel manner to ensure timely processing and delivery to interested subscribers. Dataflow architectures based on event-based design have served well in such applications because events support asynchrony, loose coupling, and helps build resilient, responsive and scalable applications. However, a unified programming model for event processing and distribution that can naturally compose the processing stages in a dataflow while exploiting the inherent parallelism available in the environment and computation is still lacking. To that end, we investigate the benefits of blending Reactive Programming with data distribution frameworks for building distributed, reactive, and high-performance stream-processing applications. Specifically, we present insights from our study integrating and evaluating Microsoft .NET Reactive Extensions (Rx) with OMG Data Distribution Service (DDS), which is a standardsbased publish/subscribe middleware suitable for demanding industrial IoT applications. Several key insights from both qualitative and quantitative evaluation of our approach are presented.

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“…The data sources may provide any type of events like social interactions over a given social network, sensor measurements from a smart city application, logs entries from a network monitor, etc. Appel et al (2012) . Fig.…”

Section: Stream Processing Enginesmentioning

confidence: 99%