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<p>Storage systems play a vital part in modern IT systems. As the volume of data grows
explosively and greater requirement on storage performance and reliability is put
forward, effective and efficient design and operation of storage systems become increasingly
complicated. </p><p>Such efforts would benefit significantly from the availability
of quantitative analysis techniques that facilitate comparison of different system
designs and configurations and provide projection of system behavior under potential
operational scenarios. The techniques should be able to capture the system details
that are relevant to the system measures of interest with adequate accuracy, and they
should allow efficient solution so that they can be employed for multiple scenarios
and for dynamic system reconfiguration. </p><p>This dissertation develops a set of
quantitative analysis methods for modern storage systems using stochastic modeling
techniques. The presented models cover several of the most prevalent storage technologies,
including RAID, cloud storage and replicated storage, and investigate some major issues
in modern storage systems, such as storage capacity planning, provisioning and backup
planning. Quantitative investigation on important system measures such as reliability,
availability and performance is conducted, and for this purpose a variety of modeling
formalisms and solution methods are employed based on the matching of the underlying
model assumptions and nature of the system aspects being studied. One of the primary
focuses of the model development is on solution efficiency and scalability of the
models to large systems. The accuracy of the developed models are validated through
extensive simulation.</p>
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