Multi-objectives firefly algorithm for task offloading in the edge-fog-cloud computing
This study proposes a two-stage strategy to ensure the successful execution of critical tasks. In the first stage, an Enhanced Task Offloading (ETO) algorithm is introduced to determine the appropriate computing layer—edge, fog, or cloud—for offloading incomplete tasks. The algorithm makes this d...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers
2024
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/114425/1/114425.pdf http://psasir.upm.edu.my/id/eprint/114425/ https://ieeexplore.ieee.org/document/10738798/ |
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| Summary: | This study proposes a two-stage strategy to ensure the successful execution of critical
tasks. In the first stage, an Enhanced Task Offloading (ETO) algorithm is introduced to determine the
appropriate computing layer—edge, fog, or cloud—for offloading incomplete tasks. The algorithm makes
this decision by assessing the availability of idle computing resources relative to the task’s computational
requirements. Additionally, it verifies the status of the server (on/off) before offloading; if the server is
unavailable, the algorithm proceeds to check the next layer. In the second stage, the strategy employs a
Multi-objective Firefly (MFA) algorithm to assign the optimal computational device within the selected
layer. Experimental simulations compare the proposed strategy with a benchmark task offloading algorithm.
The results demonstrate the superiority of the proposed strategy, achieving reductions in energy consumption
and delay and maximizing resource utilization compared to the baseline algorithms. |
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