7th International Conference on Performance Evaluation Methodologies and Tools

Research Article

Risk-Aware SLA Negotiation

  • @INPROCEEDINGS{10.4108/icst.valuetools.2013.254396,
        author={Mohamed Lamine Lamali and Helia Pouyllau and Johanne Cohen and Anne Bouillard and Dominique Barth},
        title={Risk-Aware SLA Negotiation},
        proceedings={7th International Conference on Performance Evaluation Methodologies and Tools},
        publisher={ICST},
        proceedings_a={VALUETOOLS},
        year={2014},
        month={1},
        keywords={sla negotiation quality of service reputation (max +)-algebras markov chains learning algorithms},
        doi={10.4108/icst.valuetools.2013.254396}
    }
    
  • Mohamed Lamine Lamali
    Helia Pouyllau
    Johanne Cohen
    Anne Bouillard
    Dominique Barth
    Year: 2014
    Risk-Aware SLA Negotiation
    VALUETOOLS
    ACM
    DOI: 10.4108/icst.valuetools.2013.254396
Mohamed Lamine Lamali1,*, Helia Pouyllau2, Johanne Cohen3, Anne Bouillard4, Dominique Barth5
  • 1: Alcatel-Lucent Bell Labs France
  • 2: Thales Research & Technology France
  • 3: CNRS
  • 4: ENS / INRIA
  • 5: University of Versailles-Saint-Quentin
*Contact email: mohamed_lamine.lamali@alcatel-lucent.com

Abstract

In order to assure Quality of Service (QoS) connectivity, Network Service Providers (NSPs) negotiate Service Level Agreements (SLAs). However, a committed SLA might fail to respect its QoS promises. In such a case, the customer is refunded. To maximize their revenues, the NSPs must deal with risks of SLA violations, which are correlated to their network capacities. Due to the complexity of the problem, we first study a system with one NSP provider and give a method to compute its risk-aware optimal strategy using $(\max,+)$-algebras. Using the same method, we study the case where two NSPs collaborate and the case where they compete, and we derive the Price of Anarchy. This method provides optimal negotiation strategies but, when modeling customers' reaction to SLA failure, analytical results do not hold. Hence, we propose a learning framework that chooses the NSP risk-aware optimal strategy under failures capturing the impact of reputation. Finally, by simulation, we observe how the NSP can benefit from such a framework.