Online Training Flow Scheduling for Geo-Distributed Machine Learning Jobs Over Heterogeneous and Dynamic Networks

Fan, L; Zhang, X; Zhao, Y; Sood, K; Yu, S

Online Training Flow Scheduling for Geo-Distributed Machine Learning Jobs Over Heterogeneous and Dynamic Networks

Fan, L Zhang, X Zhao, Y Sood, K Yu, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive Communications and Networking, 2024, 10, (1), pp. 277-291
Issue Date:: 2024-02-01

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Field	Value	Language
dc.contributor.author	Fan, L
dc.contributor.author	Zhang, X
dc.contributor.author	Zhao, Y
dc.contributor.author	Sood, K
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-03-12T04:15:11Z
dc.date.available	2024-03-12T04:15:11Z
dc.date.issued	2024-02-01
dc.identifier.citation	IEEE Transactions on Cognitive Communications and Networking, 2024, 10, (1), pp. 277-291
dc.identifier.issn	2372-2045
dc.identifier.issn	2332-7731
dc.identifier.uri	http://hdl.handle.net/10453/176562
dc.description.abstract	Geo-Distributed Machine Leaning (Geo-DML) has been a promising technology, which performs collaborative learning across geographically dispersed data centers (DCs) with privacy-preserving over Wide Area Networks (WANs). Unfortunately, the limited and heterogeneous WAN bandwidth poses significant challenges to the performance of Geo-DML systems, leading to increased communication overhead and affecting the revenue of ISPs eventually. In particular, when multiple online jobs coexist in Geo-DML systems, the competition for bandwidth between training flows of different jobs aggravates this negative impact. To alleviate it, this paper investigates the problem of online training flow scheduling for Geo-DML jobs. We first formulate the studied problem as an Linear Programming (LP) model with the objective of maximizing the revenue of ISPs. Then, we propose an online traffic scheduling algorithm called Training Flow Adaptive Steering (TFAS), which exploits a primal-dual framework, tailored for efficient resource allocation of jobs to schedule training flows, such that system resources are maximally utilized and training procedures can be expedited and completed in a timely manner. Meanwhile, we conduct rigorous theoretical analysis to guarantee that the proposed algorithm can achieve a good competitive ratio. Extensive evaluation results demonstrate that our algorithm performs well and outperforms commonly adopted solutions 36.2%-49.4% in average.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Cognitive Communications and Networking
dc.relation.isbasedon	10.1109/TCCN.2023.3326331
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4604 Cybersecurity and privacy
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Online Training Flow Scheduling for Geo-Distributed Machine Learning Jobs Over Heterogeneous and Dynamic Networks
dc.type	Journal Article
utslib.citation.volume	10
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
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-12T04:15:10Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	1

Abstract:

Geo-Distributed Machine Leaning (Geo-DML) has been a promising technology, which performs collaborative learning across geographically dispersed data centers (DCs) with privacy-preserving over Wide Area Networks (WANs). Unfortunately, the limited and heterogeneous WAN bandwidth poses significant challenges to the performance of Geo-DML systems, leading to increased communication overhead and affecting the revenue of ISPs eventually. In particular, when multiple online jobs coexist in Geo-DML systems, the competition for bandwidth between training flows of different jobs aggravates this negative impact. To alleviate it, this paper investigates the problem of online training flow scheduling for Geo-DML jobs. We first formulate the studied problem as an Linear Programming (LP) model with the objective of maximizing the revenue of ISPs. Then, we propose an online traffic scheduling algorithm called Training Flow Adaptive Steering (TFAS), which exploits a primal-dual framework, tailored for efficient resource allocation of jobs to schedule training flows, such that system resources are maximally utilized and training procedures can be expedited and completed in a timely manner. Meanwhile, we conduct rigorous theoretical analysis to guarantee that the proposed algorithm can achieve a good competitive ratio. Extensive evaluation results demonstrate that our algorithm performs well and outperforms commonly adopted solutions 36.2%-49.4% in average.

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http://hdl.handle.net/10453/176562