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

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posted on 21.04.2022, 13:43 authored by Leila IsmailLeila Ismail, Huned Materwala, Hossam S. Hassanein

The Internet of Vehicles over Vehicular Ad-hoc Networks is an emerging technology enabling the development of smart city applications focused on improving traffic safety, traffic efficiency, and the overall driving experience. These applications have stringent requirements detailed in 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, and respecting a single SLA constraint. They do not consider the impact of overlapped multi-requests 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 random offloading, and baseline genetic-based approaches. Results show QoS-SLA-AGA executes the requests 1.22 times faster on average compared to the random offloading approach and with 59.9% fewer SLA violations. In contrast, the baseline genetic-based approach increases the requests’ performance by 1.14 times, with 19.8% more SLA violations.

Funding

This research was funded by the National Water and Energy Center of the United Arab Emirates University (Grant 31R215). Corresponding author: Leila Ismail (e-mail: leila@uaeu.ac.ae, leilaism@gmail.com).

History

Email Address of Submitting Author

leila@uaeu.ac.ae

ORCID of Submitting Author

0000-0003-0946-1818

Submitting Author's Institution

United Arab Emirates University

Submitting Author's Country

United Arab Emirates