Performance isolation is widely applied in compute heavy and cluster-oriented infrastructures to reduce the impact of the noisy neighbor effect. However, the degree of isolation is widely unclear and relies on operational experience. Cluster operators have strong incentives to get detailed insights on the degree of isolation as they need to guarantee specific SLA, or need to make sure workloads cannot degrade each other. This work presents a method and framework from previous works to determine the isolation capability of isolation approaches in a deterministic and comparable way. These insights can be used for capacity planning, workload consolidation to reduce total cost of ownership, or for the implementation of business models such as cloud computing. We further show some first measurement results and discuss whether performance isolation is applicable to the domain of high-performance computing.