Last update:
Apr 1, 2000
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Using NetLogger for Performance Analysis of the BABAR Data Analysis System
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Brian Tierney1,
Dan Gunter1,
Bob Jacobsen1,
David Quarie1,
Jacek Becla2
- Lawrence Berkeley National Lab
- Stanford Linear Accelerator (SLAC)
Speaker:
Brian Tierney
Developers and users of high-performance distributed systems often
observe performance problems, the reasons for which
can be manifold and are frequently not obvious. Bottlenecks can
occur in any of the components along the paths through which the
data flow: the applications, the operating systems, the hosts, or the
network. We have developed a methodology, known as NetLogger,
for detailed, end-to-end, top-to-bottom monitoring and analysis of all
components. Distributed application components are instrumented to
perform precision timestamping and event logging every critical point
(e.g.: all I/O and any significant computation). The events are correlated with the host and
network monitoring to characterize the performance of all aspects of the system in detail during
actual operation.
NetLogger includes a set of tools which make it easy for distributed
applications to log interesting events , and includes tools for host
and network monitoring. The approach is novel
in that it combines network, host, and application-level monitoring to
provide a complete view of the entire system. The NetLogger Toolkit
also includes tools that allow for exploratory, interactive, real-time
analysis of the log data.
NetLogger instrumentation is being added to the computing systems
that are used at SLAC for the BaBar Project. The computing environment for
the BaBar data analysis is very complex, and consists of a large number of components. At the time
of this writing, the performance of the
BaBar data analysis system is very inconsistent: the amount of time
to perform the same analysis varies by up to a factor of 10. NetLogger
instrumentation is being added to the BaBar environment, and we
hope to use NetLogger to diagnose the causes of these performance
variations.
Presentation: | Short Paper: |
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