The rapid growth in the demand for cloud computing data presents a performance challenge for both software\nand hardware architects. It is important to analyze and characterize the data processing performance for a given\ncloud cluster and to evaluate the performance bottlenecks in a cloud cluster that contribute to higher or lower\ncomputing processing time. In this paper, we implement a detailed performance analysis and characterization for\nHadoop K-means iterations by scaling different processor micro-architecture parameters and comparing performance\nusing Intel and AMD processors. This leads to the analysis of the underlying hardware in a cloud cluster servers to\nenable optimization of software and hardware to achieve maximum performance possible. We also propose a\nperformance estimation model that estimates performance for Hadoop K-means iterations by modeling different\nprocessor micro-architecture parameters. The model is verified to predict performance with less than 5 % error margin\nrelative to a measured baseline.
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