This paper describes a system for monitoring jobs on large computational clusters. The aim is to extract information that is most useful for understanding the complete life-cycle of a job, combining and organising data from multiple sources. Information is taken from the batch scheduler and from collectors running on each node. These collect information about processes associated with the jobs as well as general operating system and device statistics.

Heuristics are applied to extract information that could help a client tune job submission strategy, to provide better throughput on this cluster and to determine how effectively the provisioned resources are being utilised. Data is stored for post-mortem analysis and data-mining by other tools. Ways of utilising this service in a grid computing environment are discussed.