On-Demand Computation Offloading Architecture in Fog Networks
With the advent of the Internet-of-Things (IoT), end-devices have been served as sensors, gateways, or local storage equipment. Due to their scarce resource capability, cloud-based computing is currently a necessary companion. However, raw data collected at devices should be uploaded to a cloud serv...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-09-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/8/10/1076 |
| _version_ | 1798040168209317888 |
|---|---|
| author | Yeonjin Jin HyungJune Lee |
| author_facet | Yeonjin Jin HyungJune Lee |
| author_sort | Yeonjin Jin |
| collection | DOAJ |
| description | With the advent of the Internet-of-Things (IoT), end-devices have been served as sensors, gateways, or local storage equipment. Due to their scarce resource capability, cloud-based computing is currently a necessary companion. However, raw data collected at devices should be uploaded to a cloud server, taking a significantly large amount of network bandwidth. In this paper, we propose an on-demand computation offloading architecture in fog networks, by soliciting available resources from nearby edge devices and distributing a suitable amount of computation tasks to them. The proposed architecture aims to finish a necessary computation job within a distinct deadline with a reduced network overhead. Our work consists of three elements: (1) resource provider network formation by classifying nodes into stem or leaf depending on network stability, (2) task allocation based on each node’s resource availability and soliciting status, and (3) task redistribution in preparation for possible network and computation losses. Simulation-driven validation in the iFogSim simulator demonstrates that our work achieves a high task completion rate within a designated deadline, while drastically reducing unnecessary network overhead, by selecting only some effective edge devices as computation delegates via locally networked computation. |
| first_indexed | 2024-04-11T22:03:45Z |
| format | Article |
| id | doaj.art-fbb83a3b1cdc4d66a59e5bf010111087 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T22:03:45Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-fbb83a3b1cdc4d66a59e5bf0101110872022-12-22T04:00:48ZengMDPI AGElectronics2079-92922019-09-01810107610.3390/electronics8101076electronics8101076On-Demand Computation Offloading Architecture in Fog NetworksYeonjin Jin0HyungJune Lee1Department of Computer Science and Engineering, Ewha Womans University, Seoul 03760, KoreaDepartment of Computer Science and Engineering, Ewha Womans University, Seoul 03760, KoreaWith the advent of the Internet-of-Things (IoT), end-devices have been served as sensors, gateways, or local storage equipment. Due to their scarce resource capability, cloud-based computing is currently a necessary companion. However, raw data collected at devices should be uploaded to a cloud server, taking a significantly large amount of network bandwidth. In this paper, we propose an on-demand computation offloading architecture in fog networks, by soliciting available resources from nearby edge devices and distributing a suitable amount of computation tasks to them. The proposed architecture aims to finish a necessary computation job within a distinct deadline with a reduced network overhead. Our work consists of three elements: (1) resource provider network formation by classifying nodes into stem or leaf depending on network stability, (2) task allocation based on each node’s resource availability and soliciting status, and (3) task redistribution in preparation for possible network and computation losses. Simulation-driven validation in the iFogSim simulator demonstrates that our work achieves a high task completion rate within a designated deadline, while drastically reducing unnecessary network overhead, by selecting only some effective edge devices as computation delegates via locally networked computation.https://www.mdpi.com/2079-9292/8/10/1076computation offloadingin-network resource allocationfog networksedge computing |
| spellingShingle | Yeonjin Jin HyungJune Lee On-Demand Computation Offloading Architecture in Fog Networks Electronics computation offloading in-network resource allocation fog networks edge computing |
| title | On-Demand Computation Offloading Architecture in Fog Networks |
| title_full | On-Demand Computation Offloading Architecture in Fog Networks |
| title_fullStr | On-Demand Computation Offloading Architecture in Fog Networks |
| title_full_unstemmed | On-Demand Computation Offloading Architecture in Fog Networks |
| title_short | On-Demand Computation Offloading Architecture in Fog Networks |
| title_sort | on demand computation offloading architecture in fog networks |
| topic | computation offloading in-network resource allocation fog networks edge computing |
| url | https://www.mdpi.com/2079-9292/8/10/1076 |
| work_keys_str_mv | AT yeonjinjin ondemandcomputationoffloadingarchitectureinfognetworks AT hyungjunelee ondemandcomputationoffloadingarchitectureinfognetworks |