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In service oriented systems, Quality of Service (QoS) is a service selection driver. Users evaluate QoS at run time to address their service invocation to the most suitable provider. Thus, QoS has a direct impact on providers' revenues. However, QoS requirements are difficult to satisfy because of the high variability of Internet workloads. Workload variability cannot be accommodated with traditional capacity planning and allocation practices, but requires autonomic computing techniques. Autonomic computing involves two tightly inter-related problems, namely, a short-term resource allocation problem and a long-term capacity planning problem. Capacity planning requires an investment that should be balanced by the revenues obtained through resource allocation. In this paper, we provide a comprehensive framework modelling both problems. The short-term resource allocation problem is analyzed in depth. The paper proposes an optimization model that identifies the optimal resource allocation by maximizing a provider's revenues while satisfying customers QoS constraints and minimizing resource usage cost. Preliminary computational experiments are presented to support the effectiveness of our approach.