Grid Management Support by means of Collaborative Learning Agents

The complex and dynamic settings of grid environments lead to challenges on their operational maintenance. The growth of these environments in terms of size and usage requires supporting systems to be of a more sophisticated level. Contemporary tools lack the ability to relate and infer events. Communication across organizational domains and interoperability between existing monitoring tools is subject to improvement. In this paper we present an information system, based on collaborative agents, that supports system administrators in monitoring the grid. While observing log files, the agents learn patterns about job-traffic in their own local domain of the grid and share information to provide global or multi-domain overviews. The agents represent their knowledge in the form of deterministic finite automata (DFA). We discuss our collaborative learning mechanism and show the results of our experiments with data of two grid-sites. Our system generated job-traffic overviews that gave new insights in the performance of the grid environment.