In this paper we study the call admission control problem to optimize the network operators revenue guaranteeing quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a semi-Markov decision process, and we use a model based reinforcement learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learn it on-line. We will show how our policy provides better solution than a classic greedy algorithm.

A Model Based RL Admission Control Algorithm for Next Generation Networks

DI GIORGIO, ALESSANDRO;SURACI, VINCENZO
2009-01-01

Abstract

In this paper we study the call admission control problem to optimize the network operators revenue guaranteeing quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a semi-Markov decision process, and we use a model based reinforcement learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learn it on-line. We will show how our policy provides better solution than a classic greedy algorithm.
2009
9781424434701
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11389/438
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? ND
social impact