Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A Distributed Fuzzy Optimal Decision Making Strategy for Task Offloading in Edge Computing Environment
AU - Behera, Sasmita Rani
AU - Panigrahi, Niranjan
AU - Bhoi, Sourav Kumar
AU - Bilal, Muhammad
AU - Sahoo, Kshira Sagar
AU - Kwak, Daehan
PY - 2023/4/5
Y1 - 2023/4/5
N2 - With the technological evolution of mobile devices, 5G and 6G communication and users' demand for new generation applications viz. face recognition, image processing, augmented reality, etc., has accelerated the new computing paradigm of Mobile Edge Computing (MEC). It operates in close proximity to users by facilitating the execution of computational-intensive tasks from devices through offloading. However, the offloading decision at the device level faces many challenges due to uncertainty in various profiling parameters in modern communication technologies. Further, with the increase in the number of profiling parameters, the fuzzy-based approaches suffer inference searching overheads. In this context, a fuzzy-based approach with an optimal inference strategy is proposed to make a suitable offloading decision. The proposed approach utilizes the Classification and Regression Tree (CART) mechanism at the inference engine with reduced time complexity of O (|V|2log2| L|)), as compared to O (| L ||V|) of state-of-the-art, conventional fuzzy-based offloading approaches, and has been proved to be more efficient. The performance of the proposed approach is evaluated and compared with contemporary offloading algorithms in a python-based fog and edge simulator, YAFS. The simulation results show a reduction in average task processing time, average task completion time, energy consumption, improved server utilization, and tolerance to latency and delay sensitivity for the offloaded tasks in terms of reduced task failure rates.
AB - With the technological evolution of mobile devices, 5G and 6G communication and users' demand for new generation applications viz. face recognition, image processing, augmented reality, etc., has accelerated the new computing paradigm of Mobile Edge Computing (MEC). It operates in close proximity to users by facilitating the execution of computational-intensive tasks from devices through offloading. However, the offloading decision at the device level faces many challenges due to uncertainty in various profiling parameters in modern communication technologies. Further, with the increase in the number of profiling parameters, the fuzzy-based approaches suffer inference searching overheads. In this context, a fuzzy-based approach with an optimal inference strategy is proposed to make a suitable offloading decision. The proposed approach utilizes the Classification and Regression Tree (CART) mechanism at the inference engine with reduced time complexity of O (|V|2log2| L|)), as compared to O (| L ||V|) of state-of-the-art, conventional fuzzy-based offloading approaches, and has been proved to be more efficient. The performance of the proposed approach is evaluated and compared with contemporary offloading algorithms in a python-based fog and edge simulator, YAFS. The simulation results show a reduction in average task processing time, average task completion time, energy consumption, improved server utilization, and tolerance to latency and delay sensitivity for the offloaded tasks in terms of reduced task failure rates.
KW - Computation offloading
KW - decision-making
KW - fuzzy logic
KW - MEC
U2 - 10.1109/ACCESS.2023.3262611
DO - 10.1109/ACCESS.2023.3262611
M3 - Journal article
AN - SCOPUS:85151562008
VL - 11
SP - 33189
EP - 33204
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -