Modern data centers that provide Internet-scale services are stadium-size structures housing tens of thousands of heterogeneous devices (server clusters, net- working equipment, power and cooling infrastructures) that must operate continuously and reliably. As part of their operation, these devices produce large amounts of data in the form of event and error logs that are essential not only for identifying problems but also for improving data center efficiency and management. These activities em- ploy data analytics and often exploit hidden statistical patterns and correlations among different factors present in the data. Uncovering these patterns and correlations is chal- lenging due to the sheer volume of data to be analyzed. This paper presents BiDAl, a prototype "log-data analysis framework" that incorporates various Big Data tech- nologies to simplify the analysis of data traces from large clusters. BiDAl is written in Java with a modular and extensible architecture so that different storage backends (currently, HDFS and SQLite are supported), as well as different analysis languages (current implementation supports SQL, R and Hadoop MapReduce) can be easily se- lected as appropriate. We present the design of BiDAl and describe our experience using it to analyze several public traces of Google data clusters for building a simula- tion model capable of reproducing observed behavior.
['Alkida Balliu', 'Dennis Olivetti', 'Ozalp Babaoglu', 'Moreno Marzolla', 'Alina Sîrbu']
BiDAl: Big Data Analyzer for Cluster Traces