JVM characterization framework for workload generated as per machine learning benckmark and spark framework

• Published in: 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) pp. 1598 - 1602
• Main Authors: Chidambaram, Saravan, Saraswati, Sujoy, Ramachandra, Ranganath, Huttanagoudar, Jayashree B., Hema, N., Roopalakshmi, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2016
• Subjects: Apache Spark; Benchmark testing; Big-Data; Clustering algorithms; In-Memory Analytics; Java; Java Virtual Machine; Machine Learnig; Machine learning algorithms; Monitoring; Sparks
• DOI: 10.1109/RTEICT.2016.7808102

Today there are plenty of frameworks to assist the development of Big-data applications. Computation and Storage are two major activities in these applications. Spark framework has replaced Map-Reduce in Hadoop, which is the preferred analytics engine for Big-data applications. Java Virtual Machine (JVM) is used as execution platform irrespective of which framework is used for development. In the production environment it is essential to monitor the health of application to gain better performance. The parameters like memory usage, CPU utilization and frequency of Garbage Collection etc., will help to decide on the health of application. In this paper a framework is proposed to characterize the JVM behavior to monitor the health of application. Workload generated by running Machine Learning algorithms available in Spark Benchmark Suite.