Task shape classification and workload characterization of google cluster trace

作者：Md. Rasheduzzaman, Md. Amirul Islam, Tasvirul Islam, Tahmid Hossain, Rashedur M. Rahman · 年份：2014 · DOI：10.1109/iadcc.2014.6779441 · 被引用次数：24 · 研究领域：Cloud Computing and Resource Management、Data Stream Mining Techniques、IoT and Edge/Fog Computing

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.