* QoS-SLA-Aware Artificial Intelligence Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing System for the Internet of Vehicles Leila Ismail * , Member, IEEE, Huned Materwala, and Hossam S. Hassanein, Fellow, IEEE Abstract— Internet of Vehicles (IoV) over Vehicular Ad-hoc Networks (VANETS) is an emerging technology enabling the development of smart cities applications for safer, efficient, and pleasant travel. These applications have stringent requirements expressed in Service Level Agreements (SLAs). Considering vehicles limited computational and storage capabilities, applications requests are offloaded into an integrated edge-cloud computing system. Existing offloading solutions focus on optimizing applications Quality of Service (QoS) while respecting a single SLA constraint. They do not consider the impact of overlapped requests processing. Very few contemplate the varying speed of a vehicle. This paper proposes a novel Artificial Intelligence (AI) QoS-SLA-aware genetic algorithm (GA) for multi-request offloading in a heterogeneous edge-cloud computing system, considering the impact of overlapping requests processing and dynamic vehicle speed. The objective of the optimization algorithm is to improve the applications' Quality of Service (QoS) by minimizing the total execution time. The proposed algorithm integrates an adaptive penalty function to assimilate the SLAs constraints in terms of latency, processing time, deadline, CPU, and memory requirements. Numerical experiments and comparative analysis are achieved between our proposed QoS- SLA-aware GA, random, and GA baseline approaches. The results show that the proposed algorithm executes the requests 1.22 times faster on average compared to the random approach with 59.9% less SLA violations. While the GA baseline approach increases the performance of the requests by 1.14 times, it has 19.8% more SLA violations than our approach. Index Terms—Artificial Intelligence (AI), Cloud Computing, Computation Offloading, Constrained Optimization, Edge Computing, Genetic Algorithm (GA), Intelligent Transportation System, Internet of Things (IoT), Internet of Vehicles (IoV), Quality of Service (QoS), Service Level Agreement (SLA), Vehicular Ad-hoc Network (VANET) * This paragraph of the first footnote will contain the date on which you submitted your paper for review, which is populated by IEEE. It is IEEE style to display support information, including sponsor and financial support acknowledgment, here and not in an acknowledgment section at the end of the article. For example, “This work was supported in part by the U.S. Department of Commerce under Grant BS123456.” The name of the corresponding author appears after the financial information, e.g. (Corresponding author: M. Smith). Here you may also indicate if authors contributed equally or if there are co-first authors. I. INTRODUCTION nternet of Vehicles (IoV) over Vehicular Ad-hoc Networks (VANETS) are self-organizing networks of vehicles for data exchange between mobile vehicles and infrastructure [1]. Vehicles act as smart nodes having sensing, computing, storage, and networking capabilities [2], [3]. Data exchange is realized using vehicle-to-vehicle (V2V), vehicle-to-roadside (V2R), vehicle-to-infrastructure (V2I), vehicle-to-cloud (V2C), and vehicle-to-pedestrian (V2P) communication. Through ubiquitous data dissemination and processing, IoV provides mechanisms to develop applications for safe, and comfortable driving, pleasant travel, and efficient traffic management [4], such as accident prevention, multi-media, infotainment, image processing and pattern recognition in autonomous driving, and real-time navigation. However, the limited computation and storage capabilities of vehicles hinders the deployment of these compute-intensive and time-critical applications. To respect applications Service Level Agreement (SLA) in terms of processing and resource requirements, vehicular cloud computing [5] has been introduced that enables execution of compute-intensive vehicles requests on remote cloud servers [6]. However, cloud may violate latency requirements for communication-bound applications due to long-distance data transmission between vehicles and the cloud. Consequently, Vehicular Edge Computing (VEC) [7] has emerged that pushes cloud services to the edge of the radio access network close to the mobile vehicles, reducing the communication delay. However, the VEC servers (deployed within Roadside Units (RSUs)) violate the stringent deadline constraints of compute- intensive applications due to limited computing capabilities compared to cloud servers. Consequently, it becomes crucial to introduce mechanisms to offload vehicles requests into an integrated edge-cloud computing system to respect SLA Leila Ismail and Huned Materwala are with the Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory, Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University (UAEU), Al Ain, Abu Dhabi, 15551, United Arab Emirates (UAE), and with National Water and Energy Center, UAEU, Al Ain, Abu Dhabi, 15551, UAE ( * Correspondence: Leila Ismail, e-mail: leila@uaeu.ac.ae). Hossam S. Hassanein is with the School of Computing, Queen’s University, Ontario, Kingston, Canada. I