Please use this identifier to cite or link to this item:
|Title:||Reinforcement learning-based dynamic bandwidth provisioning for quality of service in differentiated services networks|
|Authors:||Tham, C.-K. |
Chee-Kin Hui, T.
|Keywords:||Adaptive bandwidth provisioning|
Continuous-space reinforcement learning
Quality of service
|Source:||Tham, C.-K., Chee-Kin Hui, T. (2005-09-15). Reinforcement learning-based dynamic bandwidth provisioning for quality of service in differentiated services networks. Computer Communications 28 (15) : 1741-1751. ScholarBank@NUS Repository. https://doi.org/10.1016/j.comcom.2004.12.018|
|Abstract:||The issue of bandwidth provisioning for Per Hop Behavior (PHB) aggregates in Differentiated Services (DiffServ) networks is imperative for differentiated QoS to be achieved. This paper proposes an adaptive provisioning scheme that determines at regular intervals the amount of bandwidth to provision for each PHB aggregate, based on traffic conditions and feedback received about the extent to which QoS is being met. The scheme adjusts parameters to minimize a penalty function that is based on the QoS requirements agreed upon in the service level agreement (SLA). The novel use of a continuous-space, gradient-descent reinforcement learning algorithm enables the scheme to work effectively without accurate traffic characterization or any assumption about the network model. Using ns-2 simulations, we show that the algorithm is able to converge to a policy that provisions bandwidth such that QoS requirements are satisfied. © 2005 Elsevier B.V. All rights reserved.|
|Source Title:||Computer Communications|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 8, 2018
WEB OF SCIENCETM
checked on Feb 7, 2018
checked on Mar 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.