The pointwise stationary approximation for M1/M1/s
Management Science
Computational complexity of loss networks
Theoretical Computer Science - Special issue on probabilistic modelling
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Linear Optimization
Introduction to Linear Optimization
A critically loaded multirate link with trunk reservation
Queueing Systems: Theory and Applications
Order-Based Cost Optimization in Assemble-to-Order Systems
Operations Research
Queueing Systems: Theory and Applications
The Erlang model with non-poisson call arrivals
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Stochastic analysis of multiserver systems
ACM SIGMETRICS Performance Evaluation Review
Revisiting stochastic loss networks: structures and algorithms
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Improved approximations for the Erlang loss model
Queueing Systems: Theory and Applications
Performance management of IT services delivery
ACM SIGMETRICS Performance Evaluation Review
Provisioning for large scale loss network systems with applications in cloud computing
ACM SIGMETRICS Performance Evaluation Review
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We consider a capacity planning optimization problem in a general theoretical framework that extends the classical Erlang loss modeland related stochastic loss networks to support time-varying workloads. The time horizon consists of a sequence of coarse time intervals, each of which involves a stochastic loss network under a fixed multi-class workload that can change in a general manner from one interval to the next. The optimization problem consists of determining the capacities for each time interval that maximize a utility function over the entire time horizon, finite or infinite, where rewards gained from servicing customers are offset by penalties associated with deploying capacities in an interval and with changing capacities among intervals. We derive a state-dependent optimal policy within the context of a particular limiting regime of the optimization problem, and we prove this solution to be a symptotically optimal. Then, under fairly mild conditions, we prove that a similar structural property holds for the optimal solution of the original stochastic optimization problem, and we show how the optimal capacities comprising this solution can be efficiently computed.