Meaningful Automated Statistical Analysis of Large Computational Clusters

Abstract

As clusters utilizing commercial off-the-shelf technology have grown from tens to thousands of nodes and typical job sizes have likewise increased, much effort has been devoted to improving the scalability of message-passing fabrics, schedulers, and storage. Largely ignored, however, has been the issue of predicting node failure, which also has a large impact on scalability. In fact, more than ten years into cluster computing, we are still managing this issue on a node-by-node basis even though available diagnostic data has grown immensely. We have built a tool that uses the statistical similarity of the large number of nodes in a cluster to infer the health of each individual node. In the poster, we first present real data and statistical calculations as foundational material and justification for our claims of similarity. Next we present our methodology and its implications for early notification of deviation from normal behavior, problem diagnosis, automatic code restart via interaction with scheduler, and airflow distribution monitoring in the machine room. A framework addressing scalability is discussed briefly. Lastly, we present case studies showing how our methodology has been used to detect aberrant nodes whose deviations are still far below the detection level of traditional methods. A summary of the results of the case studies appears below