A tool for monitoring resource usage in large scale supercomputing clusters

Large scale computer clusters have during the last years become dominant for making computations in applications where extremely high computation capacity is required. The clusters consist of a large set of normal servers, interconnected with a fast network. As each node runs its own instance of the operating system, and each node is working, in that sense autonomously, supervising the whole cluster is a challenge. To get an overview of the efficency and utilization of the system, one cannot only look at one computer. It is necessary to monitor all nodes to get a good view on how the cluster behaves. Monitoring performance counters in a large scale computation cluster implies many difficulties. How can samples of performance metrics be made available for an operator? How can samples of performance metrics be stored? How can a large set of samples of performance metrics be visualized in a meaningful way? In this thesis it will be discussed how such a monitoring system can be implemented, what problems one may encounter and possible solutions