Energy-SLA-aware genetic algorithm for edge–cloud integrated computation offloading in vehicular networks

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 charac...

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Veröffentlicht in:Future generation computer systems Jg. 135; S. 205 - 222
Hauptverfasser: Materwala, Huned, Ismail, Leila, Shubair, Raed M., Buyya, Rajkumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.10.2022
Schlagworte:
Computation offloading
Edge–cloud computing
Energy-efficiency
Evolutionary genetic optimization algorithm
Quality of service (QoS)
Vehicular Ad Hoc Networks (VANET)
Vehicular Ad Hoc Networks (VANET)
Quality of service (QoS)
Edge–cloud computing
Computation offloading
Energy-efficiency
Evolutionary genetic optimization algorithm
ISSN:0167-739X, 1872-7115
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Zusammenfassung: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 that the proposed algorithm saves 2.97 times and 1.37 times more energy than 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. •Evolutionary genetic algorithm for computation offloading is proposed.•Integrated edge–cloud computing system for VANET is considered.•The objective is to reduce energy consumption of edge–cloud integrated platform.•QoS constraints are handled using adaptive penalty function.•Algorithm saves energy while preserving SLA.
ISSN:0167-739X
1872-7115
DOI:10.1016/j.future.2022.04.009