Proceedings Article10.1109/IPDPSW.2019.00146
An Edge-Based Framework for Enabling Data-Driven Pipelines for IoT Systems
Eduard Gibert Renart,Daniel Balouek-Thomert,Manish Parashar +2 more
- 20 May 2019
- pp 885-894
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TL;DR: An IoT Edge Framework that extends cloud capabilities to local devices and provides a programming model for deciding what, when, and where data get collected and processed, and the architectural design of R-Pulsar is discussed.
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Abstract: Due to the proliferation of the Internet of Things (IoT) paradigm, the number of devices connected to the Internet is growing. These devices are generating unprecedented amounts of data at the edges of the infrastructure. Although the generated data provides great potential, identifying and processing relevant data points hidden in streams of unimportant data, and doing this in near real time, remains a significant challenge. Existing stream processing platforms require the data to be transported to the cloud for processing, resulting in latencies that can prevent timely decision making or may reduce the amount of data processed. To tackle this problem, we designed an IoT Edge Framework, called R-Pulsar, that extends cloud capabilities to local devices and provides a programming model for deciding what, when, and where data get collected and processed. In this paper, we discuss motivating use cases and the architectural design of R-Pulsar. We have deployed and tested R-Pulsar on embedded devices (Raspberry Pi and Android phone) and present an experimental evaluation that demonstrates that R-Pulsar can enable timely data analytics by effectively leveraging edge and cloud resources.
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Citations
RES: real-time video stream analytics using edge enhanced clouds
Muhammad Intizar Ali,Ashiq Anjum,Omer Rana,Ali Reza Zamani,Daniel Balouek-Thomert,Manish Parashar +5 more
TL;DR: RealEdgeStream (RES) is proposed an edge enhanced stream analytics system for large-scale, high performance data analytics and takes 49% less time and saves 99% bandwidth compared to a centralized cloud-only based approach.
58
Harnessing the Computing Continuum for Urgent Science
Daniel Balouek-Thomert,Ivan Rodero,Manish Parashar +2 more
- 23 Nov 2020
TL;DR: Urgent science describes time-critical, data-driven scientific work-flows that can leverage distributed data sources in a timely way to facilitate important decision making.
RES: Real-Time Video Stream Analytics Using Edge Enhanced Clouds
01 Apr 2022
TL;DR: RealEdgeStream as mentioned in this paper proposes an edge enhanced stream analytics system for large-scale, high performance data analytics, which reduces the amount of data by filtering low-value stream objects using configurable rules and uses deep learning inference to perform analytics on the streams of interest.
Towards a Methodology for Building Dynamic Urgent Applications on Continuum Computing Platforms
01 Nov 2022
TL;DR: In this article , the authors present a methodology for incorporating contextual information into the application logic while taking into consideration the heterogeneity of the underlying platform and the unpredictability of the data, in order to integrate heterogeneous data with time-sensitive systems.
Towards a Methodology for Building Dynamic Urgent Applications on Continuum Computing Platforms
Daniel Balouek-Thomert,Eddy Caron,Laurent Lefèvre,Manish Parashar +3 more
- 01 Nov 2022
TL;DR: In this article , the authors present a methodology for incorporating contextual information into the application logic while taking into consideration the heterogeneity of the underlying platform and the unpredictability of the data, in order to integrate heterogeneous data with time-sensitive systems.
5
References
The many faces of publish/subscribe
TL;DR: This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization to better identify commonalities and divergences with traditional interaction paradigms.
•Journal Article
Apache flink : Stream and batch processing in a single engine
Paris Carbone,Paris Carbone,Asterios Katsifodimos,Asterios Katsifodimos,Stephan Ewen,Volker Markl,Volker Markl,Seif Haridi,Seif Haridi,Kostas Tzoumas +9 more
TL;DR: This paper discusses the approach to achieve high throughput for transactional query processing while allowing concurrent analytical queries, and presents its approach to distributed snapshot isolation and optimized two-phase commit protocols.
Fog Computing: A Platform for Internet of Things and Analytics
Flavio Bonomi,Rodolfo A. Milito,Preethi Natarajan,Jiang Zhu +3 more
- 01 Jan 2014
TL;DR: This chapter proposes a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing, and pays attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge.
1.3K
•Book
Space-filling curves
Hans Sagan
- 01 Jan 1994
TL;DR: The subject of space-filling curves has generated a great deal of interest in the 100 years since the first such curve was discovered by Peano as discussed by the authors, but there have been no comprehensive treatment of the subject since Siepinsky's in 1912.
Fog Computing: Helping the Internet of Things Realize Its Potential
TL;DR: Fog computing is designed to overcome limitations in traditional systems, the cloud, and even edge computing to handle the growing amount of data that is generated by the Internet of Things.
1K