Graph-Theoretic Analyses and Model Reduction for an Open Jackson Queueing Network
A graph-theoretic analysis of the steady-state behavior of an open Jackson queueing network is developed. In particular, a number of queueing-network performance metrics are shown to exhibit a spatial dependence on local drivers (e.g. increments to local exogenous arrival rates), wherein the impacts fall off across graph cutsets away from a target queue. This graph-theoretic analysis is also used to motivate a structure-preserving model reduction algorithm, and an algorithm that exactly matches performance statistics of the original model is proposed. The graph-theoretic results and model-reduction method are evaluated via simulations of an example queueing-network model.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Approximation rates of quantum neural networks for periodic functions via Jackson's inequality
Quantum neural networks (QNNs) are an analog of classical neural networks in the world of quantum computing, which are represented by a unitary matrix with trainable parameters. Inspired by the universal approximation pr…
Both the validity of the cultural tightness index and the association with creativity and order are spurious -- a comment on Jackson et al
It was recently suggested in a study published in Nature Human Behaviour that the historical loosening of American culture was associated with a trade-off between higher creativity and lower order. To this end, Jackson e…
Cultural Vocal Bursts Intensity PredictionBeing Central on the Cheap: Stability in Heterogeneous Multiagent Centrality Games
We study strategic network formation games in which agents attempt to form (costly) links in order to maximize their network centrality. Our model derives from Jackson and Wolinsky's symmetric connection model, but allow…
q-Neurons: Neuron Activations based on Stochastic Jackson's Derivative Operators
We propose a new generic type of stochastic neurons, called $q$-neurons, that considers activation functions based on Jackson's $q$-derivatives with stochastic parameters $q$. Our generalization of neural network archite…
Comment on Jackson and Sonnenschein (2007) "Overcoming Incentive Constraints by Linking Decisions"
We correct a bound in the definition of approximate truthfulness used in the body of the paper of Jackson and Sonnenschein (2007). The proof of their main theorem uses a different permutation-based definition, implicitly…