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Performance Analysis of Java Virtual Machine for Machine Learning Workloads using Apache Spark

Now a day's data is growing very rapidly, where processing and analyzing data to get useful information is the main task. There are many big data processing tools and framework such as Hadoop, Hive, Cassandra etc. Spark is one of the fastest big data processing framework in cluster computation. Basic Idea is to analyze the performance of java virtual machine (JVM) [1], by characterizing java virtual machine using SparkBench benchmark on Apache Spark™ [2]. Java virtual machine is a core execution platform for spark application. When we run the spark application on java virtual machine, its behavior is affected, which needs to be monitored to analyze the JVM performance. Here we are considering Machine Learning workloads like K-Means, Matrix Factorization and Logistic Regression. Main goal here is to analyze the machine learning workloads end to end across the cluster, with respect to following parameters such as garbage collection, memory such as heap usage, CPU process time. Characterization of JVM is done with spark cluster setup and HDFS is used as storage with distributed Hadoop cluster setup.