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Given a group of heterogeneous blade servers in a cloud computing environment or a data center of a cloud computing provider, each having its own size and speed and its own amount of preloaded special tasks, we are facing the problem of optimal distribution of generic tasks over these blade servers, such that the average response time of generic tasks is minimized. Such performance optimization is important for a cloud computing provider to efficiently utilize all the available resources and to deliver the highest quality of service. We develop a queueing model for a group of heterogeneous blade servers, and formulate and solve the optimal load distribution problem of generic tasks for multiple heterogeneous blade servers in a cloud computing environment in two different situations, namely, special tasks with and without higher priority. Extensive numerical examples and data are demonstrated and some important observations are made. It is found that server sizes, server speeds, task execution requirement, and the arrival rates of special tasks all have significant impact on the average response time of generic tasks, especially when the total arrival rate of generic tasks is large. It is also found that the server size heterogeneity and the server speed heterogeneity do not have much impact on the average response time of generic tasks. Furthermore, larger (smaller, respectively) heterogeneity results in shorter (longer, respectively) average response time of generic tasks.