| Summary: | To ensure the quality of important services, high availability is critical. One aspect to be considered in availability is the downtime of the system, which can be measured in time to recover from failures. In this report we investigate current research on the subject of repair time and the possibility to estimate this metric based on relevant parameters such as hardware, the type of fault and so on. We thoroughly analyze a set of data containing 43 000 failure traces from Los Alamos National Laboratory on 22 different cluster organized systems. To enable the analysis we create and use a program which parses the raw data, sorts and categorizes it based on certain criteria and formats the output to enable visualization. We analyze this data set in consideration of type of fault, memory size, processor quantity and at what time repairs were started and completed. We visualize our findings of number of failures and average times of repair dependent on the different parameters. For different faults and time of day we also display the empirical cumulative distributionfunction to give an overview of the probability for different times of repair. The failures are caused by a variety of different faults, where hardware and software are most frequently occurring. These two along with network faults have the highest average downtime. Time of failure proves important since both day of week and hour of day shows patterns that can be explained by for example work schedules. The hardware characteristics of nodes seem to affect the repair time as well, how this correlation works is although difficult to conclude. Based on the data extracted we suggest two simple methods of formulating a mathematical model estimating downtime which both prove insufficient; more research on the subject and on how the parameters affect each other is required.
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