103 lines
4.6 KiB
Markdown
103 lines
4.6 KiB
Markdown
file:: [Dautov e Distefano - 2022 - Stream Processing on Clustered Edge Devices.pdf](file://C:\Users\david\Zotero\/storage/YSV5LG7T/Dautov e Distefano - 2022 - Stream Processing on Clustered Edge Devices.pdf)
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file-path:: file://C:\Users\david\Zotero\/storage/YSV5LG7T/Dautov e Distefano - 2022 - Stream Processing on Clustered Edge Devices.pdf
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- 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
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ls-type:: annotation
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id:: 63f8bf02-91ab-437b-8f09-301e1862a8d2
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- 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
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ls-type:: annotation
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- ime-critical IoT applications and services demand for near real-time data processing and reaction, they cannot rely on (potentially outdated) results obtained by sending data over the network to a remote processing location.
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ls-type:: annotation
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- Edge Computing paradigm aims at pushing intelligence to devices that not only provide sensing and actuation resources, but also act as computational nodes in their own right.
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ls-type:: annotation
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- Stream Processing architecture to enable horizontal offloading at the edge,
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ls-type:: annotation
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- Clustered Edge Computing
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- proposed architecture aims to minimize the amount of data sent to a remote server, reduce network latency, and thus achieve faster processing results.
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ls-type:: annotation
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- For example, the initial object detection can take place immediately at the source, whereas more complex operations are undertaken on a remote server
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- resource allocation problem for optimal placement of video analytics queries in such a hierarchy is formulated
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- number of hops–a limitation
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- lack of support for pooling computing resources of multiple collocated edge devices, which only became possible with the recent advances in hardware and networking technologies.
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ls-type:: annotation
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- edge devices can be clustered and managed through middleware at run-time, thereby achieving even lower latency.
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ls-type:: annotation
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- Similar to the Cloud- and Fog-level coordination, these approaches rely on equipping edge nodes with agent-like virtual containers to enable orchestration and management
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ls-type:: annotation
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- edge devices are able to communicate with each other to split, delegate, and share processing tasks.
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- cloudlets
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- cluster initiators/coordinators and worker nodes
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- dynamic discovery, selection and management of suitable nodes at run-time.
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- tream Processing middleware for in-memory data analytics
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- dynamic clustering and task offloading at system run-time
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- dge Computing has to be enhanced with clustering techniques extending its application domain towards Clustered Edge Computing (CEC)
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