Performance Monitoring and Analysis System for MUSCLE-based Applications in PL-Grid

In this paper we present a system for the monitoring of data flow and resources usage in applications running in the MUSCLE environment. While MUSCLE provides the ability of running complex experiments, it does not support any monitoring features. By combining the monitoring functionality supported by Sem Mon and Nagios, we are able to design and implement a system for gathering and visualizing important run-time data relating to application performance. Fluent experiment execution is highly dependable on real-time collecting and presenting essential information connected to task processing. Of particular importance for monitoring system users is that the use of the system should be as easy as possible with regard to storing, observing and interpreting the monitoring data. These features are enabled by introducing ontologies into the operation of the monitoring system. In addition to the conventional monitoring activities, using ontologies makes it possible to automate the process of reasoning on performance flaws and to easily change the focus of monitoring. In the paper we will focus on the concept and some implementation details of our monitoring system, assuming that an infrastructure to support the transport and storage of performance data on the usage of resources in MUSCLE-based applications should be transparent and lightweight.