Managing memory resources is a critical task in Kubernetes deployments to ensure applications run smoothly and avoid issues like system lockups or terminated processes due to insufficient RAM. Kubernetes allows for dynamic resource management by setting memory requests in pod configurations, which reserves the necessary RAM and helps in optimal pod placement. Without these requests, Kubernetes may allocate pods to nodes with inadequate memory, potentially causing out-of-memory events. Tools like Gremlin can assist in identifying and resolving missing memory requests, enhancing system reliability. Additionally, Gremlin's platform allows for Chaos Engineering experiments to validate memory allocations and offers automated reliability assessments to proactively manage resource risks. Implementing both memory requests and limits is suggested to define the minimum and maximum resources a pod can consume, contributing to system stability and efficiency.