Energy-QoS-SLA-Aware Green Computing in Sustainable Internet of Vehicles

The growing demand for green computing is driven by the increasing power consumption in vehicles- edge-cloud infrastructure of the Internet of Vehicles (IoV), particularly in smart transportation systems within smart cities. Energy-efficient computing has become crucial to address environmental concerns and reduce costs. IoV facilitates vehicle-to-vehicle and vehicle-to-edge-cloud communication, enhancing road safety and providing essential services in smart cities. However, the energy efficiency of this infrastructure is a significant concern, with underutilized and overloaded computing systems consuming substantial energy. Energy-aware resource allocation models are essential for efficient resource utilization. Edge-cloud computing plays a vital role in IoV applications by offloading computation from vehicles to edge-cloud servers while maintaining Quality of Service (QoS) and Service Level Agreements (SLA). Despite its importance, little work has been done to optimize these conflicting requirements.

This project introduces an Artificial Intelligence model to divide and schedule applications on vehicle-edge-cloud infrastructure, and aimed to increase application efficiency, reduce energy consumption, and maintain QoS and SLA. The developed scheduler addresses an NP-hard multi-optimization problem and included an optimization algorithm. Design considerations include the ordering and connectivity of processing units and determining optimal resources for applications. The implications of this work extend to other disciplines requiring real-time distributed computing and edge-cloud systems, such as Internet-of-Things applications. 

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Contributors:  

• Dr. Leila Ismail, Associate Professor- UAEU

• Dr. Tham Chen Khong, Associate Professor, National University of Singapore 

• Prof. Rajkumar Buyya, Professor, The University of Melbourne, Australia