High-Availability as provided by fault-tolerance mechanisms comes at the price of increased overhead due to additional processing and communication, which may be a limiting factor to service performance as perceived by the clients. In order to quantify this impact and to understand the underlying mechanisms for performance degradation, this paper presents an approach for the analysis of client-centric performance metrics in cluster-based service deployment scenarios using High-Availability Middleware. The approach is based on a combination of measurement based empiric analysis under synthetically generated load patterns and simple queueing models, that allow for the extrapolation of empiric results and are used to gain insights into the underlying causes of the empiric performance behavior. The empiric and numerical results in the paper are based on an abstracted SIP-like call control service as deployed in future version of IP-based cellular networks, running on a two-node cluster system.