Realizing the Carbon-Aware Service Provision in ICT System

The ever-growing carbon emission of information infrastructure accounts for a significant proportion of the global carbon emissions. Existing studies reduce carbon consumption mainly by improving power efficiency on specific facilities or energy source structures. However, these methods do not jointly consider the impact of computation and network resource distribution on carbon emission. In this paper, we propose a data-driven scheme named EcoNet using reinforcement learning to reduce carbon emissions by jointly scheduling computation and network resources. We dynamically monitor the status of the computation and network facilities using cloud-edge collaboration and software-defined networking. Based on the collected status information, we formulate the resource scheduling problem as an optimization problem, which comprehensively considers the carbon emission, electricity price, and quality of service. The problem has high computation complexity, and we solve the problem with the proposed EcoNet to achieve efficient scheduling and near-optimal performance based on the collected network status information. The evaluation results show that EcoNet can maintain good Quality of Service and save at least 17% of the overall cost considering the electricity bills and carbon emissions.