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User-Centric Adaptation Analysis of Multi-Tenant Services

Authors:

Jesús García-galán,

Liliana Pasquale,

Pablo Trinidad,

Antonio Ruiz-CortésAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 10, Issue 4

Article No.: 24, Pages 1 - 26

https://doi.org/10.1145/2790303

Published: 13 January 2016 Publication History

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Abstract

Multi-tenancy is a key pillar of cloud services. It allows different users to share computing and virtual resources transparently, meanwhile guaranteeing substantial cost savings. Due to the tradeoff between scalability and customization, one of the major drawbacks of multi-tenancy is limited configurability. Since users may often have conflicting configuration preferences, offering the best user experience is an open challenge for service providers. In addition, the users, their preferences, and the operational environment may change during the service operation, thus jeopardizing the satisfaction of user preferences. In this article, we present an approach to support user-centric adaptation of multi-tenant services. We describe how to engineer the activities of the Monitoring, Analysis, Planning, Execution (MAPE) loop to support user-centric adaptation, and we focus on adaptation analysis. Our analysis computes a service configuration that optimizes user satisfaction, complies with infrastructural constraints, and minimizes reconfiguration obtrusiveness when user- or service-related changes take place. To support our analysis, we model multi-tenant services and user preferences by using feature and preference models, respectively. We illustrate our approach by utilizing different cases of virtual desktops. Our results demonstrate the effectiveness of the analysis in improving user preferences satisfaction in negligible time.

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Cited By

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Etedali ALung CAjila S(2021)Comparative Analysis of Genetic Algorithm and XML Filtering Technique for Multi-Tenant SaaS Configuration Management2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)10.1109/IEMCON53756.2021.9623123(0223-0228)Online publication date: 27-Oct-2021
https://doi.org/10.1109/IEMCON53756.2021.9623123
Aouzal KHafiddi HDahchour M(2019)Policy-Driven Middleware for Multi-Tenant SaaS Services ConfigurationInternational Journal of Cloud Applications and Computing10.4018/IJCAC.20191001059:4(86-106)Online publication date: Oct-2019
https://doi.org/10.4018/IJCAC.2019100105
Ardagna CAsal RDamiani EDimitrakos TEl Ioini NPahl C(2018)Certification-Based Cloud AdaptationIEEE Transactions on Services Computing10.1109/TSC.2018.2793268(1-1)Online publication date: 2018
https://doi.org/10.1109/TSC.2018.2793268
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Index Terms

User-Centric Adaptation Analysis of Multi-Tenant Services
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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Reviews

Reviewer: John S. Edwards

A method to arrive at configuration consensus in a multitenant shared-service cloud environment is presented in this paper. Section 1 discusses the multitenant cloud. The authors are interested in such systems where each member of the user community has the capability to dynamically modify or preselect various preferences. Since an activity on the part of one user might conflict with the preferences of another member of the community, the authors propose user-centric adaptations based on preference-based analyses. They introduce a four-fold process: monitoring, analysis, planning, and executing (MAPE) in terms of a loop. They use a game-theoretic mechanism to demonstrate their approach. Section 2 discusses the motivating scenario. Section 3 covers the problem. Section 4 describes a solution approach. Section 5 presents the implementation of their prototype. While this paper could be of value to workers in the field, several comments are in order. The scenario chosen as an example comprises four tenants and their preferences. The preferences are a combination of the mundane and the extraordinary. The choice between "Aero" and "Classic" is mundane. The choices among "Very frequent antivirus checks," "Frequent antivirus checks," and "Unfrequent virus checks" are strange and seemingly forced, as is the choice between "Highest firewall level" and "Medium firewall level." These choices attempt to reflect real-life conditions, but they seem artificial. Indeed, a reader could code the preferences (and the authors do in some cases) differently; for example, "Aero" and "Classic" could be coded as " x " and "not x " while the preferences "Very frequent antivirus checks," "Frequent antivirus checks," and "Unfrequent virus checks" could be coded as " y +," " y ," and "- y ." The analysis could proceed without the semantics. Three very complex figures are included purporting to indicate how the process works. Section 4.2, "Analysis," uses game theory to arrive at a solution. Unfortunately, the discussion is chock-a-block with formulae and must be either taken at face value for those who are not game theory adepts or taken for granted by adepts. The results of the game are discussed as it progresses. The authors discuss the need to extend their work and give examples of where they may choose to go. In the final section, as they point out, their approach could equally apply to "any kind of adaptive system" and they chose one based upon multitenant services. This work could be of general utility; perhaps they chose the multitenant environment to attract attention to the effort. A more abstract presentation would have been valid, but perhaps less noteworthy. Online Computing Reviews Service

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cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 10, Issue 4

Special Section on Best Papers from SEAMS 2014 and Regular Articles

February 2016

211 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2872308

Editors:
Manish Parashar
Rutgers University, USA
,
Franco Zambonelli
University of Modena e Reggio Emilia, Italy

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 January 2016

Accepted: 01 June 2015

Revised: 01 March 2015

Received: 01 October 2010

Published in TAAS Volume 10, Issue 4

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https://doi.org/10.1109/IEMCON53756.2021.9623123
Aouzal KHafiddi HDahchour M(2019)Policy-Driven Middleware for Multi-Tenant SaaS Services ConfigurationInternational Journal of Cloud Applications and Computing10.4018/IJCAC.20191001059:4(86-106)Online publication date: Oct-2019
https://doi.org/10.4018/IJCAC.2019100105
Ardagna CAsal RDamiani EDimitrakos TEl Ioini NPahl C(2018)Certification-Based Cloud AdaptationIEEE Transactions on Services Computing10.1109/TSC.2018.2793268(1-1)Online publication date: 2018
https://doi.org/10.1109/TSC.2018.2793268
Etedali ALung CAjila SVeselinovic I(2017)Automated Constraint-Based Multi-tenant SaaS Configuration Support Using XML Filtering Techniques2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2017.69(413-418)Online publication date: Jul-2017
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Ardagna CAsal RDamiani EEl Ioini NPahl CDimitrakos T(2016)A Certification Technique for Cloud Security Adaptation2016 IEEE International Conference on Services Computing (SCC)10.1109/SCC.2016.49(324-331)Online publication date: Jun-2016
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User-centric adaptation of multi-tenant services: preference-based analysis for service reconfiguration

User-centric mobile services: context provisioning and user profiling

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User-centric adaptation of multi-tenant services: preference-based analysis for service reconfiguration

User-centric mobile services: context provisioning and user profiling

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