OffDQ: An Offline Deep Learning Framework for QoS Prediction

Chattopadhyay, S; Chanda, R; Kumar, S; Adak, C

OffDQ: An Offline Deep Learning Framework for QoS Prediction

Chattopadhyay, S Chanda, R Kumar, S Adak, C

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Publisher:: ASSOC COMPUTING MACHINERY
Publication Type:: Conference Proceeding
Citation:: WWW 2022 - Proceedings of the ACM Web Conference 2022, 2022, pp. 1987-1996
Issue Date:: 2022-04-25

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Field	Value	Language
dc.contributor.author	Chattopadhyay, S
dc.contributor.author	Chanda, R
dc.contributor.author	Kumar, S
dc.contributor.author	Adak, C https://orcid.org/0000-0002-9085-2770
dc.date	2022-04-25
dc.date.accessioned	2023-04-18T06:50:03Z
dc.date.available	2023-04-18T06:50:03Z
dc.date.issued	2022-04-25
dc.identifier.citation	WWW 2022 - Proceedings of the ACM Web Conference 2022, 2022, pp. 1987-1996
dc.identifier.isbn	9781450390965
dc.identifier.uri	http://hdl.handle.net/10453/169962
dc.description.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.
dc.language	en
dc.publisher	ASSOC COMPUTING MACHINERY
dc.relation.ispartof	WWW 2022 - Proceedings of the ACM Web Conference 2022
dc.relation.ispartof	31st ACM Web Conference (WWW)
dc.relation.isbasedon	10.1145/3485447.3512107
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	OffDQ: An Offline Deep Learning Framework for QoS Prediction
dc.type	Conference Proceeding
utslib.location.activity	ELECTR NETWORK
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-18T06:50:01Z
pubs.finish-date	2022-04-29
pubs.publication-status	Published
pubs.start-date	2022-04-25

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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http://hdl.handle.net/10453/169962