Provisioning for large loss networks is a classic problem in performance, due to the fat that loss network is an important mathematical model for many applications, notably those in telephony. Lately, loss network models are utilized and extended to provide performance analysis and control for exciting new applications in statistical physics [3], workforce management [9] and cloud computing [4]. In these new studies, a loss network often serves as a crucial element in characterizing system dynamics and producing calculation for vital performance metrics. This can be seen from the application in resource provision for cloud computing. Cloud computing is rapidly gaining momentum as a new paradigm for offering computing as services via the Internet. Service provider usually offers a menu of service instances, which require the commitment of distinct resources(CPU, Memory,etc) at various amounts. Along with purchase of these instances, service level agreements (SLA) will specify the desired targets on various performance metrics that the service provider should meet. A common performance metric is service availability, defined as the percentage of time at which new service requests can be admitted into the system with their desired amount of resources fulfilled. Violation of the SLAs typically results in significant penalty. The objective of resource provisioning is to seek the balance between the resource costs and SLA penalty so that service availability can be guaranteed efficiently. We develop an integrated optimization framework to search for the optimal resource provision with SLA constraints. First, we develop a Markovian model to capture users’ flexibility on upgrade/downgrade services on demand and characterize the steady-state behavior of the offered load. Then, the multi-class multi-resource provisioning problem can be naturally mapped to a stochastic loss network model, and SLA constraints are mapped to constraints on loss probabilities. Based on Kelly’s approach for capacity planning in a loss network [6, 7, 8], we propose an optimization framework to determine resource levels that minimize the combined costs of resource and violation penalty. Since computing the exact loss probabilities is a ]P complete problem thus prohibitive for large service loads, we consider the Erlang fixed-point approximation for the blocking probabilities that has been proven to be asymptotically exact in the limiting regime as the traffic intensities and the resource capacities grow together in proportion [10, 6, 7]. We further