Abstract
Understanding workload characteristics is crucial for optimizing and improving the performance of large scale data produced by different industries. In this paper, we analyse a large scale production workload trace (version 2) [1] which is recently made publicly available by Google. We discuss statistical summary of the data. Further we perform k-means clustering to identify common groups of job. Cluster analysis provides insight into the data by dividing the objects into groups (clusters) of objects, such that objects in a cluster are more similar to each other than to the objects in other clusters. This work presents a simple technique for constructing workload characteristics and also provides production insights into understanding workload performance in cluster machine.
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