Title: A dynamic pricing model for carbon-aware compute clusters

Authors: Hari Sowrirajan; George Wang

Addresses: Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Department of Mathematics, Stanford University, Stanford, CA 94305, USA ' Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Department of Economics, Stanford University, Stanford, CA 94305, USA; Department of Physics, Stanford University, Stanford, CA 94305, USA; Department of Mathematics, Stanford University, Stanford, CA 94305, USA

Abstract: The rising demand for compute services has led to a proliferation of data centres (computer clusters), resulting in a significant environmental footprint as over 1.5% of the global power supply is consumed. In this work, we propose an incentive-compatible pricing mechanism for cloud computing providers that automatically promotes customers to submit jobs in a carbon-aware fashion. To demonstrate the efficacy of the mechanism, we have developed a power model and a job submission model to simulate the operation of compute clusters. We also employ auction theory to incorporate cluster parameterisation into a pricing mechanism. With this mechanism, strong utilisation of renewable energy and high utility for customers can be achieved by appropriately allocating high/low-priority jobs, both for synthetic as well as real-life workloads. Our work provides a possible solution, with environmental and financial benefits, for cloud computing providers who seek to minimise their carbon footprint while gaining a competitive advantage by passing the energy savings to their customers.

Keywords: pricing mechanisms; computerisation; auction theory; data centre management; renewable energy; cloud computing.

DOI: 10.1504/IJRM.2023.134672

International Journal of Revenue Management, 2023 Vol.13 No.4, pp.217 - 237

Received: 18 Nov 2022
Accepted: 12 Apr 2023
Published online: 03 Nov 2023 *

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