research-article

OffDQ: An Offline Deep Learning Framework for QoS Prediction

Authors:

Soumi Chattopadhyay,

Chandranath AdakAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1987 - 1996

https://doi.org/10.1145/3485447.3512107

Published: 25 April 2022 Publication History

Abstract

With the increasing trend of web services over the Internet, developing a robust Quality of Service (QoS) prediction algorithm for recommending services in real-time is becoming a challenge today. Designing an efficient QoS prediction algorithm achieving high accuracy, while supporting faster prediction to enable the algorithm to be integrated into a real-time system, is one of the primary focuses in the domain of Services Computing. The major state-of-the-art QoS prediction methods are yet to efficiently meet both criteria simultaneously, possibly due to the lack of analysis of challenges involved in designing the prediction algorithm. In this paper, we systematically analyze the various challenges associated with the QoS prediction algorithm and propose solution strategies to overcome the challenges, and thereby propose a novel offline framework using deep neural architectures for QoS prediction to achieve our goals. Our framework, on the one hand, handles the sparsity of the dataset, captures the non-linear relationship among data, figures out the correlation between users and services to achieve desirable prediction accuracy. On the other hand, our framework being an offline prediction strategy enables faster responsiveness. We performed extensive experiments on the publicly available WS-DREAM dataset to show the trade-off between prediction performance and prediction time. Furthermore, we observed our framework significantly improved one of the parameters (prediction accuracy or responsiveness) without considerably compromising the other as compared to the state-of-the-art methods.

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Cited By

Li JWu HHe QZhao YWang X(2024)Dynamic QoS Prediction With Intelligent Route Estimation Via Inverse Reinforcement LearningIEEE Transactions on Services Computing10.1109/TSC.2023.334248117:2(509-523)Online publication date: Mar-2024
https://doi.org/10.1109/TSC.2023.3342481
Wu ZDing DXiu YZhao YHong J(2024)Robust QoS Prediction Based on Reputation Integrated Graph Convolution NetworkIEEE Transactions on Services Computing10.1109/TSC.2023.331764217:3(1154-1167)Online publication date: May-2024
https://doi.org/10.1109/TSC.2023.3317642
Kumar SChattopadhyay SAdak C(2024)TPMCF: Temporal QoS Prediction Using Multi-Source Collaborative FeaturesIEEE Transactions on Network and Service Management10.1109/TNSM.2024.339542821:4(3945-3955)Online publication date: Aug-2024
https://doi.org/10.1109/TNSM.2024.3395428
Show More Cited By

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OffDQ: An Offline Deep Learning Framework for QoS Prediction

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Publication History

Published: 25 April 2022

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Refereed limited

Funding Sources

Science and Engineering Research Board, Department of Science and Technology, Government of India

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WWW '22

Sponsor:

SIGWEB

WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Li JWu HHe QZhao YWang X(2024)Dynamic QoS Prediction With Intelligent Route Estimation Via Inverse Reinforcement LearningIEEE Transactions on Services Computing10.1109/TSC.2023.334248117:2(509-523)Online publication date: Mar-2024
https://doi.org/10.1109/TSC.2023.3342481
Wu ZDing DXiu YZhao YHong J(2024)Robust QoS Prediction Based on Reputation Integrated Graph Convolution NetworkIEEE Transactions on Services Computing10.1109/TSC.2023.331764217:3(1154-1167)Online publication date: May-2024
https://doi.org/10.1109/TSC.2023.3317642
Kumar SChattopadhyay SAdak C(2024)TPMCF: Temporal QoS Prediction Using Multi-Source Collaborative FeaturesIEEE Transactions on Network and Service Management10.1109/TNSM.2024.339542821:4(3945-3955)Online publication date: Aug-2024
https://doi.org/10.1109/TNSM.2024.3395428
Lu TZhang XWang ZYan M(2023)A Feature Distribution Smoothing Network Based on Gaussian Distribution for QoS Prediction2023 IEEE International Conference on Web Services (ICWS)10.1109/ICWS60048.2023.00087(687-694)Online publication date: Jul-2023
https://doi.org/10.1109/ICWS60048.2023.00087
Kumar SChattopadhyay S(2022)TRQP: Trust-Aware Real-Time QoS Prediction Framework Using Graph-Based LearningService-Oriented Computing10.1007/978-3-031-20984-0_10(143-152)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-20984-0_10

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