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

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...

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Bibliographic Details
Published in2016 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
LanguageEnglish
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
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Summary: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.
DOI:10.1109/RTEICT.2016.7808102