BiDAl: Big Data Analyzer for Cluster Traces

TL;DR

BiDAl is a modular framework that leverages Big Data technologies to facilitate the analysis of large-scale cluster logs, aiding in understanding data center behavior and improving management.

Contribution

It introduces a flexible, extensible log analysis framework supporting multiple storage and analysis tools, tailored for large data center trace analysis.

Findings

Successfully analyzed Google cluster traces

Built a simulation model reproducing observed behaviors

Demonstrated ease of integrating various data analysis tools

Abstract

Modern data centers that provide Internet-scale services are stadium-size structures housing tens of thousands of heterogeneous devices (server clusters, networking 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 employ data analytics and often exploit hidden statistical patterns and correlations among different factors present in the data. Uncovering these patterns and correlations is challenging 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 technologies 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 selected 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 simulation model capable of reproducing observed behavior.