In a Kubernetes environment, managing memory usage through requests and limits is crucial to prevent memory leaks and ensure application resilience. Memory requests define the amount of RAM reserved for a pod upon deployment, while memory limits cap the maximum RAM a pod can use during its lifetime. Without proper memory limits, a pod can consume excessive memory, potentially leading to system instability as the Out of Memory (OOM) killer might terminate processes unpredictably. Setting accurate memory limits requires measuring normal memory usage and using tools like the Kubernetes Metrics API or Goldilocks. Gremlin offers a way to test these settings through chaos experiments, simulating scenarios where a pod exceeds its memory limit to observe Kubernetes' response. This proactive approach helps identify and mitigate reliability risks, a topic further explored in Gremlin's ebook "Kubernetes Reliability at Scale," which also provides insights into other potential system vulnerabilities.