AQUAMan: An Adaptive QoE-Aware Negotiation Mechanism for SaaS Elasticity Management

Client churn is a key challenge confronted by SaaS providers. Recent research in QoE suggested providers should rely on quantiles & percentiles to assess the service acceptability rate. In this article we introduce AQUAMan, an Adaptive QoE-Aware multi-agent negotiation mechanism for SaaS elasticity Management. Based on its estimation of the percentage of users finding the service acceptable and a learned model of the user negotiation strategy, AQUAMan adjusts the provider negotiation process in order to restore the desired service acceptability rate while meeting the budget limits (i.e. the cost paid to rent cloud resources) of the provider. The proposed mechanism is implemented and its results are examined and analyzed in light of comparable results.

[1]  L. Thurstone.  PSYCHOPHYSICAL ANALYSIS , 2008 .

[2]  Markus Fiedler,et al.  QoE-Based Network and Application Management , 2014, Quality of Experience.

[3]  Koen V. Hindriks,et al.  Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques , 2016, Autonomous Agents and Multi-Agent Systems.

[4]  Alexander Raake,et al.  Quality of Experience , 2014, T-Labs Series in Telecommunication Services.

[5]  T. Ll.  Psychophysical analysis. By L. L. Thurstone, 1927. , 1987 .

[6]  Guilherme Galante,et al.  A Survey on Cloud Computing Elasticity , 2012, 2012 IEEE Fifth International Conference on Utility and Cloud Computing.

[7]  Manuela Pereira,et al.  Factors Influencing Quality of Experience , 2014, Quality of Experience.

[8]  Sebastian Möller,et al.  QoE beyond the MOS: an in-depth look at QoE via better metrics and their relation to MOS , 2016, Quality and User Experience.

[9]  Emanuel Ferreira Coutinho,et al.  Elasticity in cloud computing: a survey , 2014, annals of telecommunications - annales des télécommunications.

[10]  Christophe Gravier,et al.  Survey of Elasticity Management Solutions in Cloud Computing , 2014 .

[11]  Tobias Hoßfeld,et al.  QoE beyond the MOS: Added value using quantiles and distributions , 2015, 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX).

[12]  Peter Reichl,et al.  The Logarithmic Nature of QoE and the Role of the Weber-Fechner Law in QoE Assessment , 2010, 2010 IEEE International Conference on Communications.

[13]  Christophe Gravier,et al.  Modeling User Expectations & Satisfaction for SaaS Applications Using Multi-agent Negotiation , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[14]  Tobias Hoßfeld,et al.  SOS: The MOS is not enough! , 2011, 2011 Third International Workshop on Quality of Multimedia Experience.

[15]  Christophe Gravier,et al.  Multi-agent Systems for Personalized QoE-Management , 2016, 2016 28th International Teletraffic Congress (ITC 28).