Efficient Scheduling of Vehicular Tasks on Edge Systems with Green Energy and Battery Storage

The autonomous vehicle industry is rapidly expanding, requiring significant computational resources for tasks like perception and decision-making. Vehicular edge computing has emerged to meet this need, utilizing roadside computational units (roadside edge servers) to support autonomous vehicles. Al...

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Bibliographic Details
Published inarXiv.org
Main Authors Sarkar, Suvarthi, Ray, Abinash Kumar, Sahu, Aryabartta
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 24.10.2024
Subjects
Autonomous vehicles
Batteries
Clean energy
Cloud computing
Edge computing
Power management
Real time
Resource scheduling
Roadsides
Servers
Solar energy
Storage
Task scheduling
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Summary:The autonomous vehicle industry is rapidly expanding, requiring significant computational resources for tasks like perception and decision-making. Vehicular edge computing has emerged to meet this need, utilizing roadside computational units (roadside edge servers) to support autonomous vehicles. Aligning with the trend of green cloud computing, these roadside edge servers often get energy from solar power. Additionally, each roadside computational unit is equipped with a battery for storing solar power, ensuring continuous computational operation during periods of low solar energy availability. In our research, we address the scheduling of computational tasks generated by autonomous vehicles to roadside units with power consumption proportional to the cube of the computational load of the server. Each computational task is associated with a revenue, dependent on its computational needs and deadline. Our objective is to maximize the total revenue of the system of roadside computational units. We propose an offline heuristics approach based on predicted solar energy and incoming task patterns for different time slots. Additionally, we present heuristics for real-time adaptation to varying solar energy and task patterns from predicted values for different time slots. Our comparative analysis shows that our methods outperform state-of-the-art approaches upto 40\% for real-life datasets.
ISSN:2331-8422