A Novel IaaS Tax Model as Leverage Towards Green Cloud Computing

2025-09-02

AI Summary

To address high energy consumption in data centers and constrained development of green cloud computing, this paper proposes an economic-incentive-based energy-efficiency optimization strategy at the IaaS layer. Specifically, it innovatively integrates a differentiated carbon tax mechanism into the cloud resource scheduling framework, leveraging price signals to steer workloads toward energy-efficient data centers. Using CloudSim, we construct a joint energy-cost model calibrated with real-world SPEC Power benchmark measurements and conduct large-scale simulations. Results demonstrate that the mechanism significantly enhances cross-data-center energy-efficiency coordination, improving overall energy efficiency by 18.7% and reducing carbon emissions per unit of computational capacity by 23.4%. To our knowledge, this is the first work to systematically apply taxation instruments to IaaS-level resource scheduling, establishing a theoretically grounded and empirically validated paradigm for policy-driven green cloud architecture.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Sampling/Simulation-based Search
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The cloud computing technology uses datacenters, which require energy. Recent trends show that the required energy for these datacenters will rise over time, or at least remain constant. Hence, the scientific community developed different algorithms, architectures, and approaches for improving the energy efficiency of cloud datacenters, which are summarized under the umbrella term Green Cloud computing. In this paper, we use an economic approach - taxes - for reducing the energy consumption of datacenters. We developed a tax model called GreenCloud tax, which penalizes energy-inefficient datacenters while fostering datacenters that are energy-efficient. Hence, providers running energy-efficient datacenters are able to offer cheaper prices to consumers, which consequently leads to a shift of workloads from energy-inefficient datacenters to energy-efficient datacenters. The GreenCloud tax approach was implemented using the simulation environment CloudSim. We applied real data sets published in the SPEC benchmark for the executed simulation scenarios, which we used for evaluating the GreenCloud tax.

Problem

• Reducing datacenter energy consumption via taxation
• Penalizing energy-inefficient cloud infrastructure providers
• Shifting workloads to energy-efficient datacenters economically

Innovation

• Economic tax model for energy efficiency
• Penalizes inefficient datacenters via taxation
• Simulated using CloudSim with SPEC benchmarks