Latency-Aware Horizontal Computation Offloading for Parallel Processing in Fog-Enabled IoT
In this article, we propose a two-step distributed horizontal architecture for computation offloading in a fog-enabled Internet of Things environment-HD-Fog-to minimize the overall energy consumption, and latency while executing hard real-time applications. HD stands for the horizontal distribution...
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| Published in: | IEEE systems journal Vol. 16; no. 2; pp. 1 - 8 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1932-8184, 1937-9234 |
| Online Access: | Get full text |
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| Abstract | In this article, we propose a two-step distributed horizontal architecture for computation offloading in a fog-enabled Internet of Things environment-HD-Fog-to minimize the overall energy consumption, and latency while executing hard real-time applications. HD stands for the horizontal distribution of the tasks in the fog layer. Each sensor in the user devices independently captures data of varying formats. Parallel execution on these data is possible based on its directed acyclic task graph (DATG), and the corresponding results facilitate the ease of decision-making. Toward this, in HD-Fog, the sensor nodes in user devices offload their tasks to a nearby fog node based on a greedy selection criterion. This fog node then further offloads the smaller subtasks, based on the DATG, among other fog nodes for parallel execution. Through extensive real-life metric-based emulation and comparison against traditional Fog and Cloud computing schemes, we observe that our approach 1) reduces the overall operational delays by 29% and 96%, and 2) offers promising speedup values. The proposed HD-Fog scheme also indicates a reduction in energy consumption by 30% compared to traditional fog computing schemes. |
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| AbstractList | In this article, we propose a two-step distributed horizontal architecture for computation offloading in a fog-enabled Internet of Things environment—HD-Fog—to minimize the overall energy consumption, and latency while executing hard real-time applications. HD stands for the horizontal distribution of the tasks in the fog layer. Each sensor in the user devices independently captures data of varying formats. Parallel execution on these data is possible based on its directed acyclic task graph (DATG), and the corresponding results facilitate the ease of decision-making. Toward this, in HD-Fog, the sensor nodes in user devices offload their tasks to a nearby fog node based on a greedy selection criterion. This fog node then further offloads the smaller subtasks, based on the DATG, among other fog nodes for parallel execution. Through extensive real-life metric-based emulation and comparison against traditional Fog and Cloud computing schemes, we observe that our approach 1) reduces the overall operational delays by 29% and 96%, and 2) offers promising speedup values. The proposed HD-Fog scheme also indicates a reduction in energy consumption by 30% compared to traditional fog computing schemes. |
| Author | Deb, Pallav Kumar Misra, Sudip Mukherjee, Anandarup |
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| Cites_doi | 10.1109/JSYST.2018.2877850 10.1109/JIOT.2018.2867351 10.1109/TVT.2019.2896586 10.1145/2248371.2248394 10.1109/TII.2019.2951206 10.1109/JIOT.2018.2838022 10.1109/TSUSC.2018.2836314 10.1049/iet-net.2015.0034 10.1109/JIOT.2018.2875599 10.1109/JSYST.2019.2923635 10.1109/TGCN.2019.2960767 10.1109/JSYST.2019.2903293 10.1109/TVT.2014.2372852 10.1109/TVT.2018.2890685 10.1109/TVT.2019.2943647 10.1109/TSIPN.2015.2448520 |
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| SubjectTerms | Cloud computing Computation offloading Computer architecture Decision making Delays distributed and parallel computing Electronic devices Energy consumption Fog computing Horizontal distribution Internet of Things Internet of Things (IoT) Network latency Nodes Optimization Parallel processing queueing theory Real-time systems Reliability Task analysis |
| Title | Latency-Aware Horizontal Computation Offloading for Parallel Processing in Fog-Enabled IoT |
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