A Framework for Cloud Workload Allotment Using Real-Time Carbon Intensity of Energy Sources

As the cloud computing is developing rapidly, the energy consumption of data centers is growing exponentially and has a significant impact on carbon emissions, which imposes great challenges on the sustainable workload management of cloud platforms. At present, most of the workload allocation strategies in cloud platform are made according to some system performance indicators, such as response time, processing ability, server utilization and so on. However, these indicators cannot reflect the carbon consumption of the power supply of the servers. In this paper, we proposed a framework for thermal-aware and carbon-efficient workload allocation which takes several thermal, energy and environmental-related metrics into account for making decisions on allocating workloads to cloud servers. In order to predict the carbon footprint for running a workload on a server, we trained a carbon emission model which is a Gradient Boosting Regressor and it takes a set of attributes into account when predicting the carbon emissions. For any incoming workload, the thermal-aware allocation engine selects the server with minimum predicted carbon emissions by making use of the model learned in training phase and a set of runtime thermal, energy and environmental metrics. Experiments are designed to evaluate the performance and compare with other allocations. Experimental results show that our framework could effectively select out the most suitable servers for workloads not only from viewpoint of carbon usage but also from the thermal perspectives, and at the same time, it also reduces the amount of cooling required and carbon emissions while keeps several performance metrics at comparable levels. Our work shows that by taking into account the thermal metrics and the current carbon intensity information, we could make sustainable scheduling decisions in cloud platforms.