Truncation of Markov chains with applications to queueing
Operations Research
Queueing networks and Markov chains: modeling and performance evaluation with computer science applications
Time-shared Systems: a theoretical treatment
Journal of the ACM (JACM)
Applied operating system concepts
Applied operating system concepts
Structured analysis approaches for large Markov chains
Applied Numerical Mathematics
Performance Modelling of Communication Networks and Computer Architectures (International Computer S
Performance Modelling of Communication Networks and Computer Architectures (International Computer S
Insensitive Bandwidth Sharing in Data Networks
Queueing Systems: Theory and Applications
Calculating the flow level performance of balanced fairness in tree networks
Performance Evaluation
Approximating optimal load balancing policy in discriminatory processor sharing systems
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
On the efficient solution of a multiserver system with two reattempt orbits
Mathematical and Computer Modelling: An International Journal
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We present a new approximation method called value extrapolation for Markov processes with large or infinite state spaces. The method can be applied for calculating any performance measure that can be expressed as the expected value of a function of the system state. Traditionally, the state distribution of a system is solved in a truncated state space and then an appropriate function is summed over the states to obtain the performance measure. In our approach, the measure is obtained directly, along with the relative values of the states, by solving the Howard equations in the MDP setting. Instead of a simple state space truncation, the relative values outside the truncated state space are extrapolated using a polynomial function. The Howard equations remain linear, hence there is no significant computational penalty. The accuracy of value extrapolation, even with a heavily truncated state space, is demonstrated using processor sharing systems and data networks as examples.