Market-Driven Collaborative Energy Management of Cloud Data Centers

Abstract

This paper proposes a privacy-aware collaborative energy management framework for cloud data centers (CDCs) operating within an electricity market environment. Each CDC’s energy agent (CDCEA) minimizes the total CDC’s energy cost by optimizing its energy profile in response to dynamic locational marginal prices and the nodal carbon intensity of the power system issued by the independent system operator (ISO), who clears the energy market. CDCEAs integrate the on-site energy resources of CDCs, such as renewable energy and energy storage systems, and leverage the spatiotemporal flexibility of computing workloads through coordination with cloud service providers (CSPs). CSPs manage privacy-preserving workload scheduling and resource allocation by collaborating with associated CDCEAs and with other CSPs, while ensuring quality of service and adherence to service-level agreements. A two-stage optimization model is developed to coordinate decision-making between the ISO and CDCEAs in energy market in the first stage, and to address the coordination of CSPs with associated CDCEAs and other CSPs in the second stage. The proposed two-stage model is solved using a three-nested distributed iterative algorithm that combines privacy-aware hierarchical and peer-to-peer ADMM methods with an analytical target cascading (ATC) approach. The method is validated using a real-world case study in Northern Virginia, USA, demonstrating its effectiveness in reducing costs and enhancing sustainability.