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The Internet of Vehicles over Vehicular Ad-hoc Networks is an emergingtechnology enabling the development of smart city applications focused onimproving traffic safety, traffic efficiency, and the overall drivingexperience. These applications have stringent requirements detailed in ServiceLevel Agreement.  Since vehicles have limited computational and storage capabilities,applications requests are offloaded onto an integrated edge-cloud computingsystem. Existing offloading solutions focus on optimizing the application'sQuality of Service (QoS) in terms of execution time, and respecting a singleSLA constraint. They do not consider the impact of overlapped multi-requestsprocessing nor the vehicle's varying speed. This paper proposes a novelArtificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA)to optimize the application's execution time for multi-request offloading in aheterogeneous edge-cloud computing system, which considers the impact ofprocessing multi-requests overlapping and dynamic vehicle speed. The proposedgenetic algorithm integrates an adaptive penalty function to assimilate the SLAconstraints regarding latency, processing time, deadline, CPU, and memoryrequirements. Numerical experiments and analysis compare our QoS-SLA-AGA torandom offloading, and baseline genetic-based approaches. Results showQoS-SLA-AGA executes the requests 1.22 times faster on average compared to therandom offloading approach and with 59.9% fewer SLA violations. In contrast,the baseline genetic-based approach increases the requests' performance by 1.14times, with 19.8% more SLA violations.

QoS-SLA-Aware Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing in Internet of Vehicles