extended-abstract

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

Authors:

Olivier Boissier,

Gauthier PicardAuthors Info & Claims

AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

Pages 1655 - 1657

Published: 08 May 2017 Publication History

Abstract

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.

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Cited By

Najjar AMualla YBoissier OPicard GSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)AQUAManProceedings of the International Conference on Web Intelligence10.1145/3106426.3106485(331-339)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106485

Index Terms

AQUAMan: An Adaptive QoE-Aware Negotiation Mechanism for SaaS Elasticity Management
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems

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cover image ACM Other conferences

AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

May 2017

1914 pages

General Chairs:
Kate Larson
University of Waterloo, Canada
,
Michael Winikoff
University of Otago, New Zealand
,
Program Chairs:
Sanmay Das
Washington University in St. Louis, USA
,
Edmund Durfee
University of Michigan, USA

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IFAAMAS

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ACM: Association for Computing Machinery

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

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Published: 08 May 2017

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AAMAS '17 Paper Acceptance Rate 127 of 457 submissions, 28%;

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

Najjar AMualla YBoissier OPicard GSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)AQUAManProceedings of the International Conference on Web Intelligence10.1145/3106426.3106485(331-339)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106485

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