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

TL;DR

This paper presents heuristics for scheduling vehicular tasks on roadside edge servers powered by solar energy and batteries, aiming to maximize revenue while adapting to energy and task variability.

Contribution

It introduces novel offline and real-time heuristics for energy-aware task scheduling in vehicular edge computing with green energy sources.

Findings

Heuristics outperform state-of-the-art methods by up to 40% on real datasets.

The approach effectively balances energy constraints and task revenue maximization.

Adaptive heuristics improve scheduling efficiency under variable solar energy conditions.

Abstract

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.