Cloud computing has enabled entirely new business models for high-performance computing. Having a dedicated local high-performance computer is still an option for some, but more are turning to cloud computing resources to fulfill their high-performance computing needs. With cloud computing it is possible to tailor your computing infrastructure to perform best for your particular type of workload by selecting the correct number of machines of each type. This paper presents an efficient algorithm to find the best set of computing resources to allocate to the workload. This research is applicable to users provisioning cloud computing resources and to data center owners making purchasing decisions about physical hardware. Studies have shown that cloud computing machines have measurable variability in their performance. Some of the causes of performance variability include small changes in architecture, location within the datacenter, and neighboring applications consuming shared network resources. The proposed algorithm models the uncertainty in the computing resources and the variability in the tasks in a many-task computing environment to find a robust number of machines of each type necessary to process the workload. In addition, reward rate, cost, failure rate, and power consumption can be optimized, as desired, to compute Pareto fronts.