Eco-friendly Power Cost Minimization for Geo-distributed Data Centers Considering Workload Scheduling

TL;DR

This paper presents a novel approach to minimize power costs and pollution in geo-distributed data centers by optimizing workload scheduling and battery management, leveraging renewable energy and a Pollution Index Function.

Contribution

It introduces a Pollution Index Function for modeling power pollution and proposes a Sequential Convex Programming algorithm for optimal workload and energy management in data centers.

Findings

Increases clean energy usage by 50-60%

Reduces power costs by 10-30%

Lowers request delay

Abstract

The rapid development of renewable energy in the energy Internet is expected to alleviate the increasingly severe power problem in data centers, such as the huge power costs and pollution. This paper focuses on the eco-friendly power cost minimization for geo-distributed data centers supplied by multi-source power, where the geographical scheduling of workload and temporal scheduling of batteries' charging and discharging are both considered. Especially, we innovatively propose the Pollution Index Function to model the pollution of different kinds of power, which can encourage the use of cleaner power and improve power savings. We first formulate the eco-friendly power cost minimization problem as a multi-objective and mixed-integer programming problem, and then simplify it as a single-objective problem with integer constraints. Secondly, we propose a Sequential Convex Programming (SCP) algorithm to find the globally optimal non-integer solution of the simplified problem, which is non-convex, and then propose a low-complexity searching method to seek for the quasi-optimal mixed-integer solution of it. Finally, simulation results reveal that our method can improve the clean energy usage up to 50\%--60\% and achieve power cost savings up to 10\%--30\%, as well as reduce the delay of requests.