Cloud application services are distributed in nature and have components across the stack working together to deliver the experience to end users. The wide adoption of microservice architecture exacerbates failure management due to increased service components. To be effective, the strategies to enhance the application service resilience need to be autonomous and developed at the service's granularity, considering its end-to-end components. However, the massive amount of observability data generated by all these components across the service stack poses a significant challenge in reacting to anomalies and restoring the service quality in real time. Identifying the most informative observability data from across the cloud service stack and timely localization of root causes of anomalies thus becomes crucial to ensure service resilience. This article presents a novel approach that considers the application service topology to select the most informative metrics across the cloud stack to support efficient, explainable, and accurate root cause identifications in case of performance anomalies. The usefulness of the selected metrics is then evaluated using the state-of-the-art Root Cause Detection (RCD) algorithm for localizing the root cause of performance anomalies. As a step towards improving the accuracy and efficiency of RCD, this article then proposes the Topology-Aware-RCD (TA-RCD) that incorporates the end-to-end application service topology in RCD. The evaluation of the failure injection studies shows that the proposed approach performs at least 2X times better on average than the state-of-the-art RCD algorithm regarding Top-3 and Top-5 recall.