Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing

Lyu, X; Tian, H; Ni, W; Zhang, Y; Zhang, P; Liu, RP

Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing

Lyu, X

Tian, H Ni, W

Zhang, Y Zhang, P Liu, RP

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Communications, 2018, 66 (6), pp. 2603 - 2616
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Lyu, X https://orcid.org/0000-0003-0404-4310	en_US
dc.contributor.author	Tian, H	en_US
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X	en_US
dc.contributor.author	Zhang, Y	en_US
dc.contributor.author	Zhang, P	en_US
dc.contributor.author	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	IEEE Transactions on Communications, 2018, 66 (6), pp. 2603 - 2616	en_US
dc.identifier.issn	0090-6778	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131772
dc.description.abstract	© 1972-2012 IEEE. Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve (1 - ∈)-approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. ∈ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an [O(∈),O(1/∈)]-tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.	en_US
dc.relation.ispartof	IEEE Transactions on Communications	en_US
dc.relation.isbasedon	10.1109/TCOMM.2018.2799937	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Energy-Efficient Admission of Delay-Sensitive Tasks for Mobile Edge Computing	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	66	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
utslib.for	0804 Data Format	en_US
pubs.embargo.period	Not known	en_US
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	66	en_US

Abstract:

© 1972-2012 IEEE. Task admission is critical to delay-sensitive applications in mobile edge computing, but is technically challenging due to its combinatorial mixed nature and consequently limited scalability. We propose an asymptotically optimal task admission approach which is able to guarantee task delays and achieve (1 - ∈)-approximation of the computationally prohibitive maximum energy saving at a time-complexity linearly scaling with devices. ∈ is linear to the quantization interval of energy. The key idea is to transform the mixed integer programming of task admission to an integer programming (IP) problem with the optimal substructure by pre-admitting resource-restrained devices. Another important aspect is a new quantized dynamic programming algorithm which we develop to exploit the optimal substructure and solve the IP. The quantization interval of energy is optimized to achieve an [O(∈),O(1/∈)]-tradeoff between the optimality loss and time complexity of the algorithm. Simulations show that our approach is able to dramatically enhance the scalability of task admission at a marginal cost of extra energy, as compared with the optimal branch and bound method, and can be efficiently implemented for online programming.

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http://hdl.handle.net/10453/131772