document: Mining for misconfigured machines in grid systems Grid systems are proving increasingly useful for managing the batch computing jobs of organizations . One well-known example is Intel , whose internally developed NetBatch system manages tens of thousands of machines . The size , heterogeneity , and complexity of grid systems make them very difficult , however , to configure . This often results in misconfigured machines , which may adversely affect the entire system . We investigate a distributed data mining approach for detection of misconfigured machines . Our Grid Monitoring System ( GMS ) non-intrusively collects data from all sources ( log files , system services , etc. ) available throughout the grid system . It converts raw data to semantically meaningful data and stores this data on the machine it was obtained from , limiting incurred overhead and allowing scalability . Afterwards , when analysis is requested , a distributed outliers detection algorithm is employed to identify misconfigured machines . The algorithm itself is implemented as a recursive workflow of grid jobs . It is especially suited to grid systems , in which the machines might be unavailable most of the time and often fail altogether .
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