• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Bankruptcy Game for Optimize Caching Resource Allocation in Small Cell Networks


Abstract

In this paper, we study the distributed cooperative caching for Internet content providers in a small cell of heterogeneous network (HetNet). A general framework based on bankruptcy game model is put forth for finding the optimal caching policy. In this framework, the small cell and different content providers are modeled as bankrupt company and players, respectively. By introducing strategic decisions into the bankruptcy game, we propose a caching value assessment algorithm based on analytic hierarchy process in the framework of bankruptcy game theory to optimize the caching strategy and increase cache hit ratio. Our analysis shows that resource utilization can be improved through cooperative sharing while considering content providers’ satisfaction. When the cache value is measured by multiple factors, not just popularity, the cache hit rate for user access is also increased. Simulation results show that our approach can improve the cache hit rate while ensuring the fairness of the distribution.


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Cite this article

[IEEE Style]
L. Zhang, G. Wang, F. Wang, "A Bankruptcy Game for Optimize Caching Resource Allocation in Small Cell Networks," KSII Transactions on Internet and Information Systems, vol. 13, no. 5, pp. 2319-2337, 2019. DOI: 10.3837/tiis.2019.05.005.

[ACM Style]
Liying Zhang, Gang Wang, and Fuxiang Wang. 2019. A Bankruptcy Game for Optimize Caching Resource Allocation in Small Cell Networks. KSII Transactions on Internet and Information Systems, 13, 5, (2019), 2319-2337. DOI: 10.3837/tiis.2019.05.005.

[BibTeX Style]
@article{tiis:22090, title="A Bankruptcy Game for Optimize Caching Resource Allocation in Small Cell Networks", author="Liying Zhang and Gang Wang and Fuxiang Wang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2019.05.005}, volume={13}, number={5}, year="2019", month={May}, pages={2319-2337}}