Datacenter carbon emissions are rising, which poses a serious issue that must be addressed quickly. We may see differences in emissions when we look at the carbon intensity of the electrical system since various areas have different energy sources. We can time the execution of workloads to occur during periods when the carbon intensity is lower by using this temporal variability. This paper aims to address the challenge of reducing carbon emissions in cloud computing by proposing a framework for workload shifting based on low carbon intensity periods in the power grid. The study focuses on four countries and their carbon production in the year 2022, calculating the carbon intensity for each country. Additionally, the paper identifies different cloud computing workloads and integrates constraints such as power, SLA, carbon emissions, and routing into the framework. The crucial factors considered during workload shifting include geo-distributed load balancing and right-sizing the data center.A simulation is developed to evaluate the proposed framework, simulating scenarios with shiftable workloads. The results are compared and analyzed, assessing the framework’s effectiveness in reducing carbon emissions while meeting the specified constraints. The findings highlight the potential benefits of workload shifting in reducing carbon emissions and improving environmental sustainability. Overall, this research contributes to the advancement of green computing and offers insights into the development of sustainable cloud computing practices.