Chimera: An Energy-Efficient and Deadline-Aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications

Pu, L; Chen, X; Mao, G; Xie, Q; Xu, J

Chimera: An Energy-Efficient and Deadline-Aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications

Pu, L Chen, X Mao, G

Xie, Q Xu, J

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Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2019, 6 (1), pp. 84 - 99
Issue Date:: 2019-02-01

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Field	Value	Language
dc.contributor.author	Pu, L	en_US
dc.contributor.author	Chen, X	en_US
dc.contributor.author	Mao, G https://orcid.org/0000-0002-3598-4949	en_US
dc.contributor.author	Xie, Q	en_US
dc.contributor.author	Xu, J	en_US
dc.date.issued	2019-02-01	en_US
dc.identifier.citation	IEEE Internet of Things Journal, 2019, 6 (1), pp. 84 - 99	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134966
dc.description.abstract	© 2014 IEEE. In this paper, we propose Chimera, a novel hybrid edge computing framework, integrated with the emerging edge cloud radio access network, to augment network-wide vehicle resources for future large-scale vehicular crowdsensing applications, by leveraging a multitude of cooperative vehicles and the virtual machine (VM) pool in the edge cloud via the control of the application manager deployed in the edge cloud. We present a comprehensive framework model and formulate a novel multivehicle and multitask offloading problem, aiming at minimizing the energy consumption of network-wide recruited vehicles serving heterogeneous crowdsensing applications, and meanwhile reconciling both application deadline and vehicle incentive. We invoke Lyapunov optimization framework to design TaskSche, an online task scheduling algorithm, which only utilizes the current system information. As the core components of the algorithm, we propose a task workload assignment policy based on graph transformation and a knapsack-based VM pool resource allocation policy. Rigorous theoretical analyses and extensive trace-driven simulations indicate that our framework achieves superior performance (e.g., 20%-68% energy saving without overstepping application deadlines for network-wide vehicles compared with vehicle local processing) and scales well for a large number of vehicles and applications.	en_US
dc.relation.ispartof	IEEE Internet of Things Journal	en_US
dc.relation.isbasedon	10.1109/JIOT.2018.2872436	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Chimera: An Energy-Efficient and Deadline-Aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	6	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	1005 Communications Technologies	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 - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access	*
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

© 2014 IEEE. In this paper, we propose Chimera, a novel hybrid edge computing framework, integrated with the emerging edge cloud radio access network, to augment network-wide vehicle resources for future large-scale vehicular crowdsensing applications, by leveraging a multitude of cooperative vehicles and the virtual machine (VM) pool in the edge cloud via the control of the application manager deployed in the edge cloud. We present a comprehensive framework model and formulate a novel multivehicle and multitask offloading problem, aiming at minimizing the energy consumption of network-wide recruited vehicles serving heterogeneous crowdsensing applications, and meanwhile reconciling both application deadline and vehicle incentive. We invoke Lyapunov optimization framework to design TaskSche, an online task scheduling algorithm, which only utilizes the current system information. As the core components of the algorithm, we propose a task workload assignment policy based on graph transformation and a knapsack-based VM pool resource allocation policy. Rigorous theoretical analyses and extensive trace-driven simulations indicate that our framework achieves superior performance (e.g., 20%-68% energy saving without overstepping application deadlines for network-wide vehicles compared with vehicle local processing) and scales well for a large number of vehicles and applications.

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http://hdl.handle.net/10453/134966