DR is then applied to the performance analysis of various multiserver systems, whose analysis involve multidimensional Markov chains. In this thesis, we primarily study fundamental natures of multiserver systems via DR, but DR also has a broad applicability in capacity planning of multiserver systems, as discussed in Chapter 8. Our analysis illuminates principles of the performance of multiserver systems, and provides lessons and guidelines that are useful in designing resource allocation policies in multiserver systems. Note that an advantage of DR is that it allows us to study the performance of multiserver systems under a wide range of environmental conditions. (For example, an arrival process can be modeled as a Markovian arrival process (MAP), service demand can be modeled as a PH distribution, and the performance can be analyzed with high accuracy under a wide range of loads, not just heavy traffic limits.) This allows us to study, for example, the impact of service demand variability on the performance, and robustness of resource allocation policies.