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Cloud computing has emerged as a highly cost-effective computation paradigm for IT enterprise applications, scientific computing, and personal data management. Because cloud services are provided by machines of various capabilities, performance, power, and thermal characteristics, it is challenging for providers to understand their cost effectiveness when deploying their systems. This article analyzes a parallelizable task in a heterogeneous cloud infrastructure with mathematical models to evaluate the energy and performance trade-off. As the authors show, to achieve the optimal performance per utility, the slowest node's response time should be no more than three times that of the fastest node. The theoretical analysis presented can be used to guide allocation, deployment, and upgrades of computing nodes for optimizing utility effectiveness in cloud computing services.