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

• URL: http://arxiv.org/abs/2202.01696v1
• Date: Fri, 21 Jan 2022 10:11:55 GMT
• Title: QoS-SLA-Aware Artificial Intelligence Adaptive Genetic Algorithm for Multi-Request Offloading in Integrated Edge-Cloud Computing System for the Internet of Vehicles
• Authors: Leila Ismail, Huned Materwala, and Hossam S. Hassanein
• Abstract summary: 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. Considering vehicles limited computational and storage capabilities, applications requests are offloaded into an integrated edge-cloud computing system. This paper proposes a novel Artificial Intelligence (AI) deadline-SLA-aware genetic algorithm (GA) for multi-request offloading in a heterogeneous edge-cloud computing system.
• Score: 14.978000952939404
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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