NDLI: Combinational Auction-Based Service Provider Selection in Mobile Edge Computing Networks

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Heli Zhang Fengxian Guo Hong Ji Chunsheng Zhu
Abstract	Via processing the computation intensive applications (apps) at the network edge, mobile edge computing (MEC) becomes a promising technology to enhance the ability of the user equipments (UEs). Most existing works usually focus on whether to offload or where to offload the apps under the premise that sufficient resources are owned by the network edge. However, the demand heterogeneity of UEs and the limitation of resources are usually failed to be considered. Since the limited resources may constrain the number of accessed UEs, how the MEC service providers (SPs) choose the UEs to serve while ensuring UEs' Quality of Service (QoS) is a key issue. Under this context, in this paper, we study the matching problem between the MEC SPs and the UEs in a multi-MEC and multi-UE scenario. Within this scenario, MEC SPs are equipped with limited wireless and computational resources. Auction theory is utilized to model the matching relationship between MEC SPs and UEs as the commodity trading. With this trading, UEs can obtain MEC service from SPs, when they successfully purchase the combinational resources (including computational and wireless resources) from SPs. To complete the auction process, a multi-round-sealed sequential combinatorial auction mechanism is proposed. The properties of the auction are proved and various simulation results are done to show that the proposed approach has better system performance compared with the existing algorithms.
e-ISSN	21693536
DOI	10.1109/ACCESS.2017.2721957
Journal	IEEE Access
Volume Number	5
Language	English
Publisher	IEEE
Publisher Date	2017-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Mobile Edge Computing Computation Offloading Mec Sp and Ue Matching Combinational Auction Demand Heterogeneity
Content Type	Text
Resource Type	Article

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