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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Bibliographic Details
Published in:IEEE systems journal Vol. 16; no. 2; pp. 1 - 8
Main Authors: Deb, Pallav Kumar, Misra, Sudip, Mukherjee, Anandarup
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:1932-8184, 1937-9234
Online Access:Get full text
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Summary: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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ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2021.3085566