TY - GEN
T1 - Workload Allocation in Fog Environment Using Multi-Objective Evolutionary Algorithms for Internet of Things
AU - Raissouli, Hafsa
AU - Ariffin, Ahmad Alauddin Bin
AU - Belhaouari, Samir Brahim
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The continuous rise in the number of IoT devices has led to an increasing importance of the fog computing paradigm. Part of the workload that should be processed is executed locally on the IoT device and the rest is offloaded and allocated to the fog nodes. This workload allocation decision should be done in a way that provides the lowest delay but while taking into account the energy consumption as well. This study presents an optimization of the workload allocation that minimizes delay and power consumption using the multi-objective evolutionary algorithms, namely, NSGA II, R-NSGA II, NSGA III, R-NSGA III and CTAEA. The experiments involve two scenarios, full transmission power of the IoT device, and half of its transmission power with varying workload sizes. The results manifested the superior performance of NSGA III and CTAEA in optimizing the allocation of tasks in fog computing environments. By demonstrating NSGA III and CTAEA's effectiveness, this findings not only advance the understanding of evolutionary algorithms but also provide practical insights for optimizing fog computing systems. This research has broader implications for improving the efficiency and performance of fog computing applications, with potential applications across various scenarios in the field.
AB - The continuous rise in the number of IoT devices has led to an increasing importance of the fog computing paradigm. Part of the workload that should be processed is executed locally on the IoT device and the rest is offloaded and allocated to the fog nodes. This workload allocation decision should be done in a way that provides the lowest delay but while taking into account the energy consumption as well. This study presents an optimization of the workload allocation that minimizes delay and power consumption using the multi-objective evolutionary algorithms, namely, NSGA II, R-NSGA II, NSGA III, R-NSGA III and CTAEA. The experiments involve two scenarios, full transmission power of the IoT device, and half of its transmission power with varying workload sizes. The results manifested the superior performance of NSGA III and CTAEA in optimizing the allocation of tasks in fog computing environments. By demonstrating NSGA III and CTAEA's effectiveness, this findings not only advance the understanding of evolutionary algorithms but also provide practical insights for optimizing fog computing systems. This research has broader implications for improving the efficiency and performance of fog computing applications, with potential applications across various scenarios in the field.
KW - Fog computing
KW - Internet of things
KW - evolutionary algorithms
KW - multi-objective optimization
KW - workload allocation
UR - http://www.scopus.com/inward/record.url?scp=85182524133&partnerID=8YFLogxK
U2 - 10.1109/CommNet60167.2023.10365185
DO - 10.1109/CommNet60167.2023.10365185
M3 - Conference contribution
AN - SCOPUS:85182524133
T3 - Proceedings - 6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023
BT - Proceedings - 6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023
A2 - El Bouanani, Faissal
A2 - Ayoub, Fouad
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023
Y2 - 11 December 2023 through 13 December 2023
ER -