Markov reward models: Application domains and solution methods
Technological advances coupled with growing demand for computation have led to increased interest in multiprocessor and distributed computing systems. There are many measures that can be used to characterize system effectiveness. This thesis is concerned with constructing and solving a particular type of analytical model, namely a Markov reward model. It focuses on applying Markov reward models to the analysis of computer systems. These models describe the behavior of the system with a continuous-time Markov chain where a reward rate is associated with each state. A common interpretation of the reward rates in a computer-system context is computational capacity, or a related performance measure. The distribution of accumulated reward or time-averaged reward over a finite time interval may be determined from the solution of the appropriate Markov reward model. The diversity of areas where Markov reward models may be used is illustrated with four examples chosen from quite different application domains.
- Research Organization:
- Duke Univ., Durham, NC (USA)
- OSTI ID:
- 5793009
- Country of Publication:
- United States
- Language:
- English
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