 Learning Automata Models for Adaptive Flow Control in Packet-Switching NetworksL. G. Mason and
XueDuo Gu
A chapter in Adaptive and Learning Systems, 1986, pp 213-227 from Springer Abstract: Abstract Performance and Stability results for three adaptive isarithmic flow control systems for packet-switching networks are described. The performance models, which are based on the BCMP theory for closed networks of queues, are exact under stationary conditions. The control architectures considered include one centralized and two decentralized schemes. The decentralized architectures include single-chain and multiple-chain cases. The controllers are modeled by L R−I learning automata. Four types of network feedback responses were considered. These are loop permit delay, loop population, loop power and path delay, where a loop includes the controller, the source queue, the network path to the message destination node and the path back to the controller. The model has been verified by Monte Carlo event simulation, thus demonstrating the feasibility of the proposed control systems and the accuracy of the analytic performance model. The various control architectures and algorithms are compared in regard to their power performance, transient response and stability characteristics. Several areas for further research are then identified. Date: 1986 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4757-1895-9_14 Ordering information: This item can be ordered from DOI: 10.1007/978-1-4757-1895-9_14 Access Statistics for this chapter More chapters in Springer Books from Springer |
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