Joint Wireless and Edge Computing Resource Management With Dynamic Network Slice Selection

Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which the wireless devices generate latency sensitive computational tasks. We address the problem of joint d...

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Veröffentlicht in:IEEE/ACM transactions on networking Jg. 30; H. 4; S. 1865 - 1878
Hauptverfasser: Josilo, Sladana, Dan, Gyorgy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation
Approximation algorithms
Completion time
Computation offloading
Computational modeling
Computational modelling
Computer games
Decentralized algorithm
decentralized algorithms
Edge computing
game theory
Heuristic algorithms
Heuristics algorithm
Job analysis
Mathematical analysis
Mixed integer
Mobile edge computing
Natural resources management
Network latency
Network slicing
resource allocation
Resource management
Resources allocation
Task analysis
Wireless communication
Wireless communications
ISSN:1063-6692, 1558-2566, 1558-2566
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Zusammenfassung:Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which the wireless devices generate latency sensitive computational tasks. We address the problem of joint dynamic assignment of computational tasks to slices, management of radio resources across slices and management of radio and computing resources within slices. We formulate the Joint Slice Selection and Edge Resource Management (JSS-ERM) problem as a mixed-integer problem with the objective to minimize the completion time of computational tasks. We show that the JSS-ERM problem is NP-hard and develop an approximation algorithm with bounded approximation ratio based on a game theoretic treatment of the problem. We use extensive simulations to provide insight into the performance of the proposed solution from the perspective of the whole system and from the perspective of individual slices. Our results show that the proposed slicing policy can achieve significant gains compared to the equal slicing policy, and that the computational complexity of the proposed task placement algorithm is approximately linear in the number of devices.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1063-6692
1558-2566
1558-2566
DOI:10.1109/TNET.2022.3156178