Learning a pricing strategy in multi-domain DWDM networks

Gurzi, Pasquale; Steenhaut, Kris; Nowé, Ann; Vrancx, Peter

doi:10.1109/lanman.2011.6076922

Cited by 1 publication

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To avoid the need for central coordination or sensitive information exchange among domains, we propose a reinforcement signal that depends only on the ISP's utility. This article builds on previous work by the authors, which investigated the possible use of CARLA to learn link prices for fixed network conditions [Gurzi et al 2011]. To deal with the nonstationary environments, we also present a particular exploration rule that improves the agent's ability to react to the changing network conditions.…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Approach for Interdomain Routing with Link Prices

Vrancx

Gurzi

Rodriguez

et al. 2015

ACM Trans. Auton. Adapt. Syst.

Self Cite

View full text Add to dashboard Cite

In today's Internet, the commercial aspects of routing are gaining importance. Current technology allows Internet Service Providers (ISPs) to renegotiate contracts online to maximize profits. Changing link prices will influence interdomain routing policies that are now driven by monetary aspects as well as global resource and performance optimization. In this article, we consider an interdomain routing game in which the ISP's action is to set the price for its transit links. Assuming a cheapest path routing scheme, the optimal action is the price setting that yields the highest utility (i.e., profit) and depends both on the network load and the actions of other ISPs. We adapt a continuous and a discrete action learning automaton (LA) to operate in this framework as a tool that can be used by ISP operators to learn optimal price setting. In our model, agents representing different ISPs learn only on the basis of local information and do not need any central coordination or sensitive information exchange. Simulation results show that a single ISP employing LAs is able to learn the optimal price in a stationary environment. By introducing a selective exploration rule, LAs are also able to operate in nonstationary environments. When two ISPs employ LAs, we show that they converge to stable and fair equilibrium strategies. . 2015. A reinforcement learning approach for interdomain routing with link prices.

show abstract

Section: Introductionmentioning

confidence: 99%