Improving J9 Java Virtual Machine with LTTng for Efficient & Effective Tracing, Yang Wang, University of New Brunswick
The ability to observe the internal operation of the J9 virtual machine is essential for effective performance tuning. To this end, tracing is an important method, which is the action of recording events from a running system with minimum performance overhead for online or off-line analysis. In this paper, we propose the integration of LTTng, an effective opensource tracing toolset, with J9 to improve its tracing functions. With this integration, the tracing component is not only decoupled from the virtual machine but also performed efficiently at both user and kernel levels to achieve a high-throughput result. To validate the integration and its impact performance, some empirical study results based on the SpecJBB2005 and SQLBenchmark are also presented.
Wang Yang is currently working as a PDF with IBM Center for Advanced Studies (CAS), Atlantic, University of New Brunswick, Canada. Before joining CAS Atlantic in May, 2012, he was a research fellow at the National University of Singapore (2010-2012). He received BS degree in Applied Mathematics from Ocean University of China, and MS and PhD (2008) from Carleton and University of Alberta, respectively.
This research is sponsored by IBM CAS (Atlantic), Canada and Atlantic Canada Opportunities Agency (ACOA).