Modeling User Expectations &amp; Satisfaction for SaaS Applications Using Multi-agent Negotiation

Najjar, Amro; Gravier, Christophe; Serpaggi, Xavier; Boissier, Olivier

doi:10.1109/wi.2016.0062

Cited by 8 publications

(10 citation statements)

References 28 publications

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“…Recent studies map QoE to multi-agent systems (MASs). In particular, QoE comes handy when modeling users' satisfaction, expectations, and the will to maximize their objective with intelligent agents [26]. Each user can be bounded with a personal agent representing his/her context and preferences and acts on his/her behalf [27].…”

Section: Quality Of Experiencementioning

confidence: 99%

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

et al. 2021

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Context. Asynchronous messaging is increasingly used to support human–machine interactions, generally implemented through chatbots. Such virtual entities assist the users in activities of different kinds (e.g., work, leisure, and health-related) and are becoming ingrained into humans’ habits due to factors including (i) the availability of mobile devices such as smartphones and tablets, (ii) the increasingly engaging nature of chatbot interactions, (iii) the release of dedicated APIs from messaging platforms, and (iv) increasingly complex AI-based mechanisms to power the bots’ behaviors. Nevertheless, most of the modern chatbots rely on state machines (implementing conversational rules) and one-fits-all approaches, neglecting personalization, data-stream privacy management, multi-topic management/interconnection, and multimodal interactions. Objective. This work addresses the challenges above through an agent-based framework for chatbot development named EREBOTS. Methods. The foundations of the framework are based on the implementation of (i) multi-front-end connectors and interfaces (i.e., Telegram, dedicated App, and web interface), (ii) enabling the configuration of multi-scenario behaviors (i.e., preventive physical conditioning, smoking cessation, and support for breast-cancer survivors), (iii) online learning, (iv) personalized conversations and recommendations (i.e., mood boost, anti-craving persuasion, and balance-preserving physical exercises), and (v) responsive multi-device monitoring interface (i.e., doctor and admin). Results. EREBOTS has been tested in the context of physical balance preservation in social confinement times (due to the ongoing pandemic). Thirteen individuals characterized by diverse age, gender, and country distribution have actively participated in the experimentation, reporting advancements in the physical balance and overall satisfaction of the interaction and exercises’ variety they have been proposed.

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Section: Quality Of Experiencementioning

confidence: 99%

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

et al. 2021

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“…A Multi-agent architecture aiming at involving the enduser into the elasticity management process is required in order to strike a balance between the provider's Quality of Business (QoBiz) and the users' Quality of Experience (QoE). EMan (Figure 1) is a one-to-many multi-agent architecture for modeling the negotiation between a SaaS provider and Service Users (SU) or end-users [7], [8]. The EMan architecture deployed in the cloud ecosystem [7] A.…”

Section: The One-to-many Negotiation Architecturementioning

confidence: 99%

“…EMan (Figure 1) is a one-to-many multi-agent architecture for modeling the negotiation between a SaaS provider and Service Users (SU) or end-users [7], [8]. The EMan architecture deployed in the cloud ecosystem [7] A. AGENTS EMan consists of three agent types: service user agents (sa i ), delegate agents (da i ) and a single coordinator agent (ca) which have been discussed subsequently.…”

Section: The One-to-many Negotiation Architecturementioning

confidence: 99%

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One-to-Many Negotiation QoE Management Mechanism for End-User Satisfaction

et al. 2021

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Quality of Experience (QoE) is defined as the measure of end-user satisfaction with the service. Existing works addressing QoE-management rely on a binary vision of end-user satisfaction. This vision has been criticized by the growing empirical evidence showing that QoE is rather a degree. This article aims to go beyond the binary vision and propose a QoE management mechanism. We propose a one-to-many negotiation mechanism allowing the provider to undertake satisfaction management: to meet fine-grained user QoE goals, while still minimizing the costs. This problem is formulated as an optimization problem, for which a linear model is proposed. For reference, a generic linear program solver is used to find the optimal solution, and an alternative heuristic algorithm is devised to improve the responsiveness when the system has to scale up with a fast growing number of users. Both are implemented and experimentally evaluated against state-of-the-art one-to-many negotiation frameworks.

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“…In this work, various algorithms have been studied in order to obtain the best trade-off between the user or SLA requirements and provider profit. [64], [65] also proposes QoE-aware management elastic approaches that try to maximize users' satisfaction without extra costs. Other examples for improving the performance are found in the research community and commercial clouds such as Rackspace [33], Scalr [36], RightScale [34].…”

Section: Purposementioning

confidence: 99%

Elasticity in Cloud Computing: State of the Art and Research Challenges

Al-Dhuraibi¹,

Paraïso

Djarallah³

et al. 2018

IEEE Trans. Serv. Comput.

309

134

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International audienceElasticity is a fundamental property in cloud computing that has recently witnessed major developments. This article reviews both classical and recent elasticity solutions and provides an overview of containerization, a new technological trend in lightweight virtualization. It also discusses major issues and research challenges related to elasticity in cloud computing. We comprehensively review and analyze the proposals developed in this field. We provide a taxonomy of elasticity mechanisms according to the identified works and key properties. Compared to other works in literature, this article presents a broader and detailed analysis of elasticity approaches and is considered as the first survey addressing the elasticity of containers

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Modeling User Expectations & Satisfaction for SaaS Applications Using Multi-agent Negotiation

Cited by 8 publications

References 28 publications

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

EREBOTS: Privacy-Compliant Agent-Based Platform for Multi-Scenario Personalized Health-Assistant Chatbots

One-to-Many Negotiation QoE Management Mechanism for End-User Satisfaction

Elasticity in Cloud Computing: State of the Art and Research Challenges

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