Online Low-Carbon Workload, Energy, and Temperature Management of Distributed Data Centers

TL;DR

This paper introduces an online, real-time method for managing workload, energy, and temperature in distributed data centers to minimize costs and carbon emissions amid uncertainties, without needing prior data.

Contribution

It presents a novel Lyapunov optimization-based approach for joint workload, energy, and temperature management in distributed data centers under uncertainty.

Findings

Reduces operational costs and carbon emissions in case studies.

Provides theoretical bounds on the optimality gap of the strategy.

Validates effectiveness through method comparison and simulations.

Abstract

Data centers have become one of the major energy consumers, making their low-carbon operations critical to achieving global carbon neutrality. Although distributed data centers have the potential to reduce costs and emissions through cooperation, they are facing challenges due to uncertainties. This paper proposes an online approach to co-optimize the workload, energy, and temperature strategies across distributed data centers, targeting minimal total cost, controlled carbon emissions, and adherence to operational constraints. Lyapunov optimization technique is adopted to derive a parametric real-time strategy that accommodates uncertainties in workload demands, ambient temperature, electricity prices, and carbon intensities, without requiring prior knowledge of their distributions. A theoretical upper bound for the optimality gap is derived, based on which a linear programming problem is proposed to optimize the strategy parameters, enhancing performance while ensuring operational constraints. Case studies and method comparison validate the proposed method's effectiveness in reducing costs and carbon emissions.