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Current generation of Internet-based services are typically hosted on largedata centers that take the form of warehouse-size structures housing tens ofthousands of servers. Continued availability of a modern data center is theresult of a complex orchestration among many internal and external actorsincluding computing hardware, multiple layers of intricate software, networkingand storage devices, electrical power and cooling plants. During the course oftheir operation, many of these components produce large amounts of data in theform of event and error logs that are essential not only for identifying andresolving problems but also for improving data center efficiency andmanagement. Most of these activities would benefit significantly from dataanalytics techniques to exploit hidden statistical patterns and correlationsthat may be present in the data. The sheer volume of data to be analyzed makesuncovering these correlations and patterns a challenging task. This paperpresents BiDAl, a prototype Java tool for log-data analysis that incorporatesseveral Big Data technologies in order to simplify the task of extractinginformation from data traces produced by large clusters and server farms. BiDAlprovides the user with several analysis languages (SQL, R and Hadoop MapReduce)and storage backends (HDFS and SQLite) that can be freely mixed and matched sothat a custom tool for a specific task can be easily constructed. BiDAl has amodular architecture so that it can be extended with other backends andanalysis languages in the future. In this paper we present the design of BiDAland describe our experience using it to analyze publicly-available traces fromGoogle data clusters, with the goal of building a realistic model of a complexdata center.

A Big Data Analyzer for Large Trace Logs