Decomposition and aggregation by class in closed queueing networks
IEEE Transactions on Software Engineering
Stochastic comparisons for non-markov processes
Mathematics of Operations Research
Stochastic ordering for Markov processes on partially ordered spaces
Mathematics of Operations Research
Aggregation/disaggregation methods for computing the stationary distribution of a Markov chain
SIAM Journal on Numerical Analysis
Numerical transient analysis of Markov models
Computers and Operations Research
Simple bounds for queueing systems with breakdowns
Performance Evaluation
An approximation method for tandem queues with blocking
Operations Research
Throughput bounds for closed queueing networks with queue-dependent service rates
Performance Evaluation
A simple throughput bound for large closed queueing networks with finite capacities
Performance Evaluation - Queueing networks with finite capacity queues
Monotonicity of performance measures in a processor sharing queue
Performance Evaluation
On product form approximations for communication networks with losses: error bounds
Annals of Operations Research - Special issue on stochastic modeling of telecommunication systems
Computable Error Bounds for Aggregated Markov Chains
Journal of the ACM (JACM)
Aggregation and Disaggregation in Queueing Networks: The Principle of Product-Form Synthesis
Proceedings of the International Workshop on Computer Performance and Reliability
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.24 |
Queueing networks are known to provide a useful modelling and evaluation tool in computer and telecommunications. Unfortunately, realistic features like finite capacities, link failures, dynamic routing and non-exponentiality usually prohibit analytic solutions.Numerical and approximate computations as well as simplifications and performance bounds for queueing networks therefore become of practical interest. These are usually based on some form of a system modification or rather a comparison of a system under different conditions. This tutorial will outline and survey a technique to conclude comparison results and error bounds for comparing performance measures of a system under different circumstances. Most notably, this includes:*perturbations *system comparisons *state space truncations or extensions *modifications to obtain simple performance bounds The advantages and disadvantages of this technique, which is based on recursive or dynamic Markov reward structures, compared to the standard stochastic comparison or sample path approach will also be outlined. To illustrate and support the results a number of practical queueing network applications will be presented. These applications cannot be solved analytically, but simple performance bounds will be provided. The applications include simple expressions for:*Jackson networks subject to breakdowns *Jackson networks with large finite buffers *a performability front-end database system *a communications example with alternate routing *a circuit-switch telecommunications model with link failures