The field of cloud computing and system-on-chip design is witnessing significant advancements in error diagnosis and cloud service optimization. Researchers are exploring innovative approaches to automate error diagnosis, leveraging machine learning and hierarchical architectures to improve accuracy and efficiency. Furthermore, there is a growing focus on optimizing cloud-native services, with novel methods being proposed to address challenges in service optimization, such as maintaining end-to-end quality of service across dynamically distributed services. Noteworthy papers in this area include: A Multi-stage Error Diagnosis for APB Transaction, which proposes an automated error diagnosis framework using a hierarchical Random Forest-based architecture, achieving an overall accuracy of 91.36%. Efficient Fault Localization in a Cloud Stack Using End-to-End Application Service Topology, which presents a novel approach that considers the application service topology to select the most informative metrics across the cloud stack, resulting in at least 2X times better performance than state-of-the-art algorithms.