QoS-SLA-aware adaptive genetic algorithm for multi-request offloading in integrated edge-cloud computing in Internet of vehicles

Abstract

The Internet of Vehicles over vehicular ad hoc network is an emerging technology enabling the development of smart applications focused on improving traffic safety, traffic efficiency, and the overall driving experience. These applications have stringent requirements detailed in the Service Level Agreement. Since vehicles have limited computational and storage capabilities, applications' requests are offloaded onto an integrated edge-cloud computing system. Existing offloading solutions focus on optimizing the application's Quality of Service (QoS) in terms of execution time while respecting a single SLA constraint. They do not consider the impact of overlapped multi-request processing nor the vehicle's varying speed. This paper proposes a novel Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA) to optimize the application's execution time for multi-request offloading in a heterogeneous edge-cloud computing system, which considers the impact of processing multi-requests overlapping and dynamic vehicle speed. The proposed genetic algorithm integrates an adaptive penalty function to assimilate the SLA constraints regarding latency, processing time, deadline, CPU, and memory requirements. Numerical experiments and analysis compare our QoS-SLA-AGA to baseline genetic-based, meta-heuristic Particle Swarm Optimization (PSO), random offloading, All Edge Computing (AEC), and All Cloud Computing (ACC) approaches. Results show QoS-SLA-AGA executes the requests 1.04, 1.23, 1.05, and 9.41 times faster on average compared to the PSO, random offloading, ACC, and AEC approaches respectively. Moreover, the proposed algorithm violates 49.58%, 60.36%, 16.26%, and 80.42% fewer SLAs compared to PSO, random, ACC, and AEC respectively. In contrast, the baseline genetic-based approach increases the requests' performance by 1.14 times, with 24.03% more SLA violations.