Title
A Supplier-Firm-Buyer Framework for Computation and Content Resource Assignment in Wireless Virtual Networks
Abstract
In recent years, the joint configuration of communication, computing and popular content resources in wireless networks has been gaining an increasing amount of attention to efficiently handle the gigantic data traffic. To effectively manage resources, the network virtualization is deemed as a promising technique in which mobile virtual network operators (MVNOs) create virtual slices to serve the requests issued by their subscribed users via obtaining contents and computing abilities from content providers and fog nodes. In this paper, the above MVNO optimization is formulated as an assignment game employing the supplier-firm-buyer game model, which gives the optimal solution of matchings among the contents, computation nodes, MVNOs, and users. Moreover, the existence of the non-empty core of such game is proved, indicating that the proposed framework is stable. In order to obtain the simple practical solution, a distributed suboptimal algorithm of reduced version of three-sided matching with size and cyclic preference (R-TMSC) is adopted. Furthermore, a greedy strategy is proposed to improve the convergence speed as well as performance of the R-TMSC scheme. The simulation results show that compared to the random allocation, a 12.97% increase in average revenue can be reached by solving the supplier-firm-buyer problem, and that the greedy R-TMSC algorithm is able to reach the similar point of the optimal value with a faster speed.
Year
DOI
Venue
2019
10.1109/TWC.2019.2921344
IEEE Transactions on Wireless Communications
Keywords
Field
DocType
Games,Resource management,Virtualization,Optimization,Wireless networks,Computational modeling
Virtualization,Revenue,Resource management,Convergence (routing),Wireless network,Wireless,Computer network,Network virtualization,Mathematics,Computation
Journal
Volume
Issue
ISSN
18
8
1536-1276
Citations 
PageRank 
References 
1
0.35
0
Authors
3
Name
Order
Citations
PageRank
Zhongyu Miao163.44
Ying Wang231639.54
Zhu Han311215760.71