In recent years, escalating concerns over environmental sustainability have highlighted the urgent need to address excessive carbon emissions, which drive climate change and its detrimental effects, including severe weather, food shortages, and increased poverty. This thesis explores the implementation of carbon-aware scheduling within a Kubernetes cluster, aiming to minimize carbon emissions, and evaluating how the performance of the system is affected. Kubernetes, an open-source platform for automating the deployment, scaling, and management of containerized applications, offers a promising framework for integrating carbon-aware strategies. With the increase in the use of computing power, data centers have become a significant contributor to global carbon emissions due to their considerable energy consumption. By integrating carbon-aware strategies into Kubernetes, this study investigates the potential for reducing the carbon footprint of microservice-based applications. Microservices is an architectural style where the application is divided into small, independent services, each handling a specific function. This allows for greater flexibility, scalability, and ease of maintenance. This thesis proposes and develops a carbon-aware scheduler that tests various applications under different traffic loads to assess its effectiveness in reducing carbon emissions. The scheduler optimally allocates workloads to different geographical locations based on carbon intensity, aiming to leverage multiple data centers across the globe. Performing experiments within a controlled testbed environment demonstrates that the scheduler is able to reduce emissions effectively, up to 28.58%, although with trade-offs in response times and system performance during migration phases. This thesis contributes to the emerging field of green computing by providing a practical approach to carbon-aware scheduling in cloud environments, highlighting the balance between environmental impact and performance metrics.