A Paradox in a Queueing Network with State-Dependent Routing and Loss
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Summary
Examples are given showing that the user optimal and asymptotic system optimal policies may differ for finite systems and that as the service rate is increased at the second stage the users optimal policy may change in such a way that the total expected cost due to loss increases.
- Type
- article
- Published
- 2007-11-05
- Cited by
- 8
- References
- 18
- Access
- Open access
- OpenAlex
- https://openalex.org/W83143185
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:722024
Keywords
Queue, Queueing theory, Computer science, Routing (electronic design automation), State (computer science)
References
- PREVALENCE OF BRAESS' PARADOX
- The Economics Of Traffic Congestion
- Non-cooperative routing in loss networks
- Asymptotically Optimal Loss Network Control
- Über ein Paradoxon aus der Verkehrsplanung
- USER-OPTIMAL STATE-DEPENDENT ROUTEING IN PARALLEL TANDEM QUEUES WITH LOSS
- Braess's paradox in a queueing network with state-dependent routing
- Alternative routeing in fully connected queueing networks
- Braess's paradox in a loss network
- How bad is selfish routing?
- Competitive routing in multiuser communication networks
- On a Paradox of Traffic Planning
- Great cities and their traffic
- The Downs-Thomson Effect in a Markov Process
- ROAD PAPER. SOME THEORETICAL ASPECTS OF ROAD TRAFFIC RESEARCH.
- Philosophical Transactions of the Royal Society of London (A)
- Some Theoretical Aspects of Road Traffic Research
- THE LAW OF PEAK-HOUR EXPRESSWAY CONGESTION
- How bad is selfish routing?
Cited by
- Asymptotically optimal control of parallel tandem queues with loss
- On the Downs-Thomson Paradox in a Self-Financing Two-Tier Queuing System
- Bio-Inspired Paradigms in Network Engineering Games
- Admission control strategies for tandem Markovian loss systems
- On Downs–Thomson paradox in two-tier service systems with a fast pass and revenue-based capacity investment
- User optimal policies for a stochastic transportation network
- Dimensioning a queue with state-dependent arrival rates
- How should rejection be used to maximize congestion while preserving idle time?
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