TY - GEN
T1 - Energy-Optimized Task Offloading with Genetic Simulated-Annealing-Based PSO for Heterogeneous Edge and Cloud Computing
AU - Yuan, Haitao
AU - Zheng, Ziyue
AU - Bi, Jing
AU - Zhang, Jia
AU - Zhou, Meng Chu
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Recent years have seen a surge in Internet of Things (IoT) technologies, with billions of mobile devices (MDs) straining limited computing and networking resources. Mobile edge computing offloads tasks from MDs to edge servers, saving energy and reducing network pressure. Edge servers provide closer services yet have fewer resources than cloud servers. A new heterogeneous edge and cloud computing paradigm combines the benefits of both. Edge servers provide close proximity services to MDs, while the cloud owns enough resources. The existence of mobile IoT devices makes it more practical to consider mobility when allocating resources of edge servers to decrease the energy consumption of the heterogeneous edge and cloud while meeting the latency needs of tasks. This work formulate a constrained energy consumption optimization problem and design a hybrid algorithm named Genetic Simulated-annealing-based particle swarm optimization (PSO) to yield a near-optimal solution. Simulation results prove that compared to genetic algorithm, PSO, simulated-annealing-based PSO, and Trex, GSPSO reduces the total energy consumption by 38.64%, 54.63%, 45.94%, and 36.21%, respectively.
AB - Recent years have seen a surge in Internet of Things (IoT) technologies, with billions of mobile devices (MDs) straining limited computing and networking resources. Mobile edge computing offloads tasks from MDs to edge servers, saving energy and reducing network pressure. Edge servers provide closer services yet have fewer resources than cloud servers. A new heterogeneous edge and cloud computing paradigm combines the benefits of both. Edge servers provide close proximity services to MDs, while the cloud owns enough resources. The existence of mobile IoT devices makes it more practical to consider mobility when allocating resources of edge servers to decrease the energy consumption of the heterogeneous edge and cloud while meeting the latency needs of tasks. This work formulate a constrained energy consumption optimization problem and design a hybrid algorithm named Genetic Simulated-annealing-based particle swarm optimization (PSO) to yield a near-optimal solution. Simulation results prove that compared to genetic algorithm, PSO, simulated-annealing-based PSO, and Trex, GSPSO reduces the total energy consumption by 38.64%, 54.63%, 45.94%, and 36.21%, respectively.
KW - Cloud computing
KW - edge computing
KW - energy optimization
KW - particle swarm optimization
KW - simulated annealing
UR - http://www.scopus.com/inward/record.url?scp=85217877277&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85217877277&partnerID=8YFLogxK
U2 - 10.1109/SMC54092.2024.10831521
DO - 10.1109/SMC54092.2024.10831521
M3 - Conference contribution
AN - SCOPUS:85217877277
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 647
EP - 652
BT - 2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024
Y2 - 6 October 2024 through 10 October 2024
ER -