TY - GEN
T1 - JVM characterization framework for workload generated as per machine learning benckmark and spark framework
AU - Chidambaram, Saravan
AU - Saraswati, Sujoy
AU - Ramachandra, Ranganath
AU - Huttanagoudar, Jayashree B.
AU - Hema, N.
AU - Roopalakshmi, R.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/5
Y1 - 2017/1/5
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85015020405
UR - https://www.scopus.com/inward/citedby.url?scp=85015020405&partnerID=8YFLogxK
U2 - 10.1109/RTEICT.2016.7808102
DO - 10.1109/RTEICT.2016.7808102
M3 - Conference contribution
AN - SCOPUS:85015020405
T3 - 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings
SP - 1598
EP - 1602
BT - 2016 IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2016
Y2 - 20 May 2016 through 21 May 2016
ER -