Distributed inter-domain SLA negotiation using Reinforcement Learning

Applications requiring network Quality of Service (QoS) (e.g. telepresence, cloud computing, etc.) are becoming mainstream. To support their deployment, network operators must automatically negotiate end-to-end QoS contracts (aka. Service Level Agreements, SLAs) and configure their networks accordingly. Other crucial needs must be considered: QoS should provide incentives to network operators, and confidentiality on topologies, resource states and committed SLAs must be respected. To meet these requirements, we propose two distributed learning algorithms that will allow network operators to negotiate end-to-end SLAs and optimize revenues for several demands while treating requests in real-time: one algorithm minimizes the cooperation between providers while the other demands to exchange more information. Experiment results exhibit that the second algorithm satisfies better customers and providers while having worse runtime performances.