Optimal Workload Allocation for Distributed Edge Clouds With Renewable Energy and Battery Storage

TL;DR

This paper presents an optimization framework for workload allocation in renewable energy-powered edge clouds, aiming to minimize costs and emissions while ensuring service quality.

Contribution

It introduces a novel optimization model integrating renewable energy and battery storage for edge clouds, addressing cost reduction and environmental impact.

Findings

The proposed model effectively reduces operational costs.

It maintains service quality while minimizing emissions.

Renewable energy and battery storage significantly influence system operations.

Abstract

This paper studies an optimal workload allocation problem for a network of renewable energy-powered edge clouds that serve users located across various geographical areas. Specifically, each edge cloud is furnished with both an on-site renewable energy generation unit and a battery storage unit. Due to the discrepancy in electricity pricing and the diverse temporal-spatial characteristics of renewable energy generation, how to optimally allocate workload to different edge clouds to minimize the total operating cost while maximizing renewable energy utilization is a crucial and challenging problem. To this end, we introduce and formulate an optimization-based framework designed for Edge Service Providers (ESPs) with the overarching goal of simultaneously reducing energy costs and environmental impacts through the integration of renewable energy sources and battery storage systems, all while maintaining essential quality-of-service standards. Numerical results demonstrate the effectiveness of the proposed model and solution in maintaining service quality as well as reducing operational costs and emissions. Furthermore, the impacts of renewable energy generation and battery storage on optimal system operations are rigorously analyzed.