EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources

TL;DR

EASE is a novel energy-aware job scheduling framework for vehicular edge networks that minimizes carbon footprint and enhances QoS by integrating predictive optimization and distributed consensus.

Contribution

It introduces a combined centralized and distributed scheduling approach for renewable-powered edge servers in vehicular networks, improving energy efficiency and QoS.

Findings

EASE achieves near carbon neutrality in energy consumption.

It outperforms four existing migration strategies in energy efficiency.

EASE improves task completion deadlines and QoS metrics.

Abstract

The energy sustainability of multi-access edge computing (MEC) platforms is here addressed by developing Energy-Aware job Scheduling at the Edge (EASE), a computing resource scheduler for edge servers co-powered by renewable energy resources and the power grid. The scenario under study involves the optimal allocation and migration of time-sensitive computing tasks in a resource-constrained internet of vehicles (IoV) context. This is achieved by tackling, as the main objective, the minimization of the carbon footprint of the edge network, whilst delivering adequate quality of service (QoS) to the end users (e.g., meeting task execution deadlines). EASE integrates i) a centralized optimization step, solved through model predictive control (MPC), to manage the renewable energy that is locally collected at the edge servers and their local computing resources, estimating their future availability, and ii) a distributed consensus step, solved via dual ascent in closed form, to reach agreement on service migrations. EASE is compared with four existing migration strategies. Quantitative results demonstrate its greater energy efficiency, which often gets close to complete carbon neutrality, while also improving the QoS.