TY - JOUR
T1 - Energy-SLA-aware genetic algorithm for edge–cloud integrated computation offloading in vehicular networks
AU - Materwala, Huned
AU - Ismail, Leila
AU - Shubair, Raed M.
AU - Buyya, Rajkumar
N1 - Funding Information:
This research was funded by the National Water and Energy Center of the United Arab Emirates University (Grant 31R215 ).
Publisher Copyright:
© 2022 The Authors
PY - 2022/10
Y1 - 2022/10
N2 - Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches. On average, the results show than the proposed algorithm saves 2.97 times and 1.37 times more energy that the random and no offloading algorithms respectively. Our algorithm has 0.3% of violations versus 52.8% and 62.8% by the random and no offloading approaches respectively. While the energy-non-SLA-aware genetic algorithm saves, on average, 1.22 times more energy than our approach, however, it violates SLAs by 159 times more than our proposed approach.
AB - Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches. On average, the results show than the proposed algorithm saves 2.97 times and 1.37 times more energy that the random and no offloading algorithms respectively. Our algorithm has 0.3% of violations versus 52.8% and 62.8% by the random and no offloading approaches respectively. While the energy-non-SLA-aware genetic algorithm saves, on average, 1.22 times more energy than our approach, however, it violates SLAs by 159 times more than our proposed approach.
KW - Computation offloading
KW - Edge–cloud computing
KW - Energy-efficiency
KW - Evolutionary genetic optimization algorithm
KW - Quality of service (QoS)
KW - Vehicular Ad Hoc Networks (VANET)
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U2 - 10.1016/j.future.2022.04.009
DO - 10.1016/j.future.2022.04.009
M3 - Article
AN - SCOPUS:85130385742
SN - 0167-739X
VL - 135
SP - 205
EP - 222
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -