research-article

AQUAMan: QoE-driven cost-aware mechanism for SaaS acceptability rate adaptation

Authors:

Olivier Boissier,

Gauthier PicardAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 331 - 339

https://doi.org/10.1145/3106426.3106485

Published: 23 August 2017 Publication History

Abstract

As more interactive and multimedia-rich applications are migrating to the cloud, end-user satisfaction and her Quality of Experience (QoE) will become a determinant factor to secure success for any Software as a Service (SaaS) provider. Yet, in order to survive in this competitive market, SaaS providers also need to maximize their Quality of Business (QoBiz) and minimize costs paid to cloud providers. However, most of the existing works in the literature adopt a provider-centric approach where the end-user preferences are overlooked. In this article, we propose the AQUAMan mechanism that gives the provider a fine-grained QoE-driven control over the service acceptability rate while taking into account both end-users' satisfaction and provider's QoBiz. The proposed solution is implemented using a multi-agent simulation environment. The results show that the SaaS provider is capable of attaining the predefined acceptability rate while respecting the imposed average cost per user. Furthermore, the results help the SaaS provider identify the limits of the adaptation mechanism and estimate the best average cost to be invested per user.

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Cited By

Zhao HTchappi IMualla YGalland SLi L(2023)An empirical probability-based strategy model for individual decision-making under time pressure when rescheduling daily activitiesPersonal and Ubiquitous Computing10.1007/s00779-023-01743-y27:5(1717-1727)Online publication date: 11-Sep-2023
https://doi.org/10.1007/s00779-023-01743-y
Najjar AMualla YSingh KPicard GCalvaresi DMalhi AGalland SFramling K(2021)One-to-Many Negotiation QoE Management Mechanism for End-User SatisfactionIEEE Access10.1109/ACCESS.2021.30716469(59231-59243)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3071646
Kampik TNajjar A(2020)Simulating, Off-Chain and On-Chain: Agent-Based Simulations in Cross-Organizational Business ProcessesInformation10.3390/info1101003411:1(34)Online publication date: 7-Jan-2020
https://doi.org/10.3390/info11010034
Show More Cited By

Index Terms

AQUAMan: QoE-driven cost-aware mechanism for SaaS acceptability rate adaptation
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
2. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems

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cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 ACM.

© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Zhao HTchappi IMualla YGalland SLi L(2023)An empirical probability-based strategy model for individual decision-making under time pressure when rescheduling daily activitiesPersonal and Ubiquitous Computing10.1007/s00779-023-01743-y27:5(1717-1727)Online publication date: 11-Sep-2023
https://doi.org/10.1007/s00779-023-01743-y
Najjar AMualla YSingh KPicard GCalvaresi DMalhi AGalland SFramling K(2021)One-to-Many Negotiation QoE Management Mechanism for End-User SatisfactionIEEE Access10.1109/ACCESS.2021.30716469(59231-59243)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3071646
Kampik TNajjar A(2020)Simulating, Off-Chain and On-Chain: Agent-Based Simulations in Cross-Organizational Business ProcessesInformation10.3390/info1101003411:1(34)Online publication date: 7-Jan-2020
https://doi.org/10.3390/info11010034
Kampik TNajjar A(2019)Integrating Multi-agent Simulations into Enterprise Application LandscapesHighlights of Practical Applications of Survivable Agents and Multi-Agent Systems. The PAAMS Collection10.1007/978-3-030-24299-2_9(100-111)Online publication date: 22-Jun-2019
https://doi.org/10.1007/978-3-030-24299-2_9
Najjar AMualla YBoissier OPicard G(2017)AQUAManProceedings of the International Conference on Web Intelligence - WI '1710.1145/3106426.3106485(331-339)Online publication date: 2017
https://doi.org/10.1145/3106426.3106485
Najjar ABoissier OPicard G(2017)Elastic & Load-Spike Proof One-to-Many Negotiation to Improve the Service Acceptability of an Open SaaS ProviderAutonomous Agents and Multiagent Systems10.1007/978-3-319-71682-4_1(1-20)Online publication date: 25-Nov-2017
https://doi.org/10.1007/978-3-319-71682-4_1

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