2.1 KiB
2.1 KiB
tags:: readingnotes date:: 01-04-2022 issn:: "2168-7161, 2372-0018" issue:: 2 doi:: 10.1109/TCC.2020.2983402 title:: @Stream Processing on Clustered Edge Devices pages:: 885-898 volume:: 10 item-type:: journalArticle access-date:: 2023-02-22T10:28:55Z original-title:: Stream Processing on Clustered Edge Devices language:: en url:: https://ieeexplore.ieee.org/document/9051851/ publication-title:: IEEE Transactions on Cloud Computing journal-abbreviation:: IEEE Trans. Cloud Comput. authors:: Rustem Dautov, Salvatore Distefano library-catalog:: DOI.org (Crossref) links:: Local library, Web library
- Abstract
- The Internet of Things continuously generates avalanches of raw sensor data to be transferred to the Cloud for processing and storage. Due to network latency and limited bandwidth, this vertical offloading model, however, fails to meet requirements of time-critical data-intensive applications which must act upon generated data with minimum time delays. To address such a limitation, this article proposes a novel distributed architecture enabling stream data processing at the edge of the network, broadening the principle of enabling processing closer to data sources adopted by Fog and Edge Computing. Specifically, this architecture extends the Apache NiFi stream processing middleware with support for run-time clustering of heterogeneous edge devices, such that computational tasks can be horizontally offloaded to peer devices and executed in parallel. As opposed to vertical offloading on the Cloud, the proposed solution does not suffer from increased network latency and is thus able to offer 5-25 times faster response time, as demonstrated by the experiments on a run-time license plate recognition system.
- Attachments
- Dautov e Distefano - 2022 - Stream Processing on Clustered Edge Devices.pdf {{zotero-imported-file YSV5LG7T, "Dautov e Distefano - 2022 - Stream Processing on Clustered Edge Devices.pdf"}}