Efficient resource management is a challenge in multi-tenant cloud environments as workloads compete for shared resources such as last-level cache and memory bandwidth, whose congestion can change dynamically as workload composition and resource demands evolve. Existing resource management approaches often rely on static resource requirements or prior workload profiling, limiting their ability to respond to changing resource contention as required in the cloud. In this paper, we leverage workload buoyancy, a runtime framework for capturing workload sensitivity to individual resources and application-level performance, to enable adaptive scheduling and resource management. We propose a holistic approach that incorporates buoyancy into workload scheduling, node-local resource allocation, and workload rescheduling throughout the workload lifecycle. We implement our approach as a Kubernetes-native prototype and evaluate it on a multi-node cluster using diverse cloud workloads under varying levels of resource contention. In a high-congestion scenario, our approach increases the percentage of workloads satisfying their SLOs from 7.74% to 87.8% on average compared with the Kubernetes baseline, while reducing resource congestion, the severity of SLO violations, and cluster fragmentation using limited rescheduling.

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