Task Offloading Control and Customized Workload Scheduling in Multi-Layer Cloud Networks

Zhang, Y; Feng, B; Tian, A; Yu, S; Zhang, H

Task Offloading Control and Customized Workload Scheduling in Multi-Layer Cloud Networks

Zhang, Y Feng, B Tian, A Yu, S

Zhang, H

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Network and Service Management, 2024, 21, (1), pp. 714-728
Issue Date:: 2024-02-01

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Feng, B
dc.contributor.author	Tian, A
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Zhang, H
dc.date.accessioned	2024-08-21T05:28:20Z
dc.date.available	2024-08-21T05:28:20Z
dc.date.issued	2024-02-01
dc.identifier.citation	IEEE Transactions on Network and Service Management, 2024, 21, (1), pp. 714-728
dc.identifier.issn	1932-4537
dc.identifier.issn	1932-4537
dc.identifier.uri	http://hdl.handle.net/10453/180477
dc.description.abstract	Recent advances in Cloud Computing have shown great power in enhancing intelligent devices to support various applications. Nevertheless, conventional Cloud Computing fails to keep up with the ever-advancing requirements of efficient task execution, mainly resulting from its drawbacks in communication delay. To this end, multi-layer cloud computing with local, edge, and remote data centers has gained high interest yet remains challenging because of the inherent complexity of cross-layer orchestration. In particular, with more participants involved, it is nontrivial to achieve customized service provision while guaranteeing system stability. Hence, we address the workload scheduling issue in the multi-layer cloud paradigm in this paper, with task offloading and service reconfiguration considered jointly. We first formulate it as a stochastic optimization problem, where statistical service requirements are imposed on queue lengths. Then, we divide the original optimization into three individual low-complex sub-problems with optimal solutions provided. To improve system performance, we introduce a request-rejecting mechanism that augments our approach with delay-optimality. Theoretical analysis confirms that our approaches can guarantee system stability and are asymptotically optimal within a small gap from the optimum. Finally, we validate the efficiency of our approaches through extensive simulation results in performance guarantees and customized workload scheduling.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Network and Service Management
dc.relation.isbasedon	10.1109/TNSM.2023.3317810
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4604 Cybersecurity and privacy
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Task Offloading Control and Customized Workload Scheduling in Multi-Layer Cloud Networks
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
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
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Centre for Cyber Security and Privacy (CCSP)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-21T05:28:17Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	1

Abstract:

Recent advances in Cloud Computing have shown great power in enhancing intelligent devices to support various applications. Nevertheless, conventional Cloud Computing fails to keep up with the ever-advancing requirements of efficient task execution, mainly resulting from its drawbacks in communication delay. To this end, multi-layer cloud computing with local, edge, and remote data centers has gained high interest yet remains challenging because of the inherent complexity of cross-layer orchestration. In particular, with more participants involved, it is nontrivial to achieve customized service provision while guaranteeing system stability. Hence, we address the workload scheduling issue in the multi-layer cloud paradigm in this paper, with task offloading and service reconfiguration considered jointly. We first formulate it as a stochastic optimization problem, where statistical service requirements are imposed on queue lengths. Then, we divide the original optimization into three individual low-complex sub-problems with optimal solutions provided. To improve system performance, we introduce a request-rejecting mechanism that augments our approach with delay-optimality. Theoretical analysis confirms that our approaches can guarantee system stability and are asymptotically optimal within a small gap from the optimum. Finally, we validate the efficiency of our approaches through extensive simulation results in performance guarantees and customized workload scheduling.

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http://hdl.handle.net/10453/180477