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This thesis describes measurement-based models based on real error-data collected on a multi-processor system. Models development from the raw error-data to the estimation of cumulative reward is described.
A workload/reliability model is developed based on low-level error and resource usage data collected on an IBM 3081 system during its normal operation in order to evaluate the resource-usage/error/recovery process in a large mainframe system. Thus, both normal and erroneous behavior of the system are modeled. The results provide an understanding of the different types of errors and recovery processes. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model the system behavior. A sensitivity analysis is performed to investigate the significance of using a semi-Markov process, as opposed to a Markov process, to model the measured system.
A software reliability model is also developed based on low-level error data from the MVS operating system running on an IBM 3081 machine to describe the software error and recovery process. The semi-Markov model developed provides a quantification of system error characteristics and the interaction between different types of errors. As an example, we provide a detailed model and analysis of multiple errors, which constitute approximately 17% of all software errors and result in considerable recovery overhead.
In addition, a measurement-based performability model based on real error-data collected is proposed. A reward function, based on the service rate and the error rate in each state, is defined in order to estimate the performability of the system and, to depict the cost of different error types and recovery procedures.
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