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Analysis of storage system workloads is important for a number of reasons. The analysis might be performed to understand the usage patterns of existing storage systems. It is very important for the architects to understand the usage patterns when designing and developing a new, or improving upon the existing design of a storage system. It is also important for a system administrator to understand the usage patterns when configuring and tuning a storage system. The analysis might also be performed to determine the relationship between any two given workloads. Before a decision is taken to pool storage resources to increase the throughput, there is need to establish whether the different workloads involved are correlated or not. Furthermore, the analysis of storage system workloads can be done to monitor the usage and to understand the storage requirements and behavior of system and application software. Another very important reason for analyzing storage system workloads, is the need to come up with correct workload models for storage system evaluation. For the evaluation, based on simulations or otherwise, to be reliable, one has to analyze, understand and correctly model the workloads. In our work we have developed a general tool, called ESSWA (Enterprize Storage System Workload Analyzer) for analyzing storage system workloads, which has a number of advantages over other storage system workload analyzers described in literature. Given a storage system workload in the form of an I/O trace file containing data for the workload parameters, ESSWA gives statistics of the data. From the statistics one can derive mathematical models in the form of probability distribution functions for the workload parameters. The statistics and mathematical models describe only the particular workload for which they are produced. This is because storage system workload characteristics are sensitive to the file system and buffer pool design and implementation, so that the results of any analysis are less broadly applicable. We experimented with ESSWA by analyzing storage system workloads represented by three sets of I/O traces at our disposal. Our results, among other things show that: I/O request sizes are influenced by the operating system in use; the start addresses of I/O requests are somewhat influenced by the application; and the exponential probability density function, which is often used in simulation of storage systems to generate inter-arrival times of I/O requests, is not the best model for that purpose in the workloads that we analyzed. We found the Weibull, lognormal and beta probability density functions to be better models.

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Settlement of a structure is a matter of great concern. Both excessive and differential settlement can cause expensive damage to buildings and must be avoided. Most methods used to estimate settlement are both deterministic in nature and are based on elastic analysis of soils. To better estimate settlement, a probabilistic estimate that uses a more in depth analysis of the behavior of soil is required. This thesis develops a new probabilistic model for estimating settlement based on a probabilistic unified soil compression model. The model is then used to estimate the settlement of an embankment. Lastly, a reliability analysis of settlement is carried out on the settlement estimate of the embankment. The new probabilistic unified soil compression model used in this thesis was developed based on a previously developed probabilistic unified soil compression model, accounting for further uncertainties into the model and correcting for errors in the model calibration. This model was calibrated using data from a site on the Venice Lagoon using a Bayesian approach. The model to estimate settlement was developed based on this probabilistic soil compression model and is unbiased in nature. Using this model, unbiased settlement estimates were obtained for an embankment also located in the Venice Lagoon. Using the developed probabilistic model for settlement, reliability analysis was carried out. This reliability analysis involved assessing the conditional probability that, for a specific load and given soil properties, a specified settlement threshold would be reached or passed. Sensitivity and importance analysis were carried out, determining which parameters and random variables have the largest impact on the fragility estimates. Lastly, a closed-formed approximation based on the Central Limit Theorem was developed to allow for easier fragility estimation.