期刊
IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 21, 期 12, 页码 4296-4309出版社
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2021.3074934
关键词
Network slicing; Business; Indium phosphide; III-V semiconductor materials; Computer architecture; Quality of service; Privacy; Network slicing; decentralized algorithm; posted price mechanism; privacy preserving; multi-tenant networks
资金
- National Science Foundation of China [U20A20173, 61772461]
- National Key Research and Development Program of China [2019YFD1101105]
- Natural Science Foundation of Zhejiang Province [LR18F020003]
- Zhejiang University Deqing Institute of Advanced technology and Industrilization
This paper proposes an online algorithm DPoS for multi-resource slicing, which is fully decentralized and low-complexity. The algorithm achieves close-to-offline-optimal performance in most cases and has low algorithmic overheads.
Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to realize network slicing in real-world business scenarios. To deal with the high algorithm complexity, privacy leakage, and unrealistic offline setting of current network slicing algorithms, in this paper we propose a fully decentralized and low-complexity online algorithm, DPoS, for multi-resource slicing. We first formulate the problem as a global social welfare maximization problem. Next, we design the online algorithm DPoS based on the primal-dual approach and posted price mechanism. In DPoS, each tenant is incentivized to make its own decision based on its true preferences without disclosing any private information to the mobile virtual network operator and other tenants. We provide a rigorous theoretical analysis to show that DPoS has the optimal competitive ratio when the cost function of each resource is linear. Extensive simulation experiments are conducted to evaluate the performance of DPoS. The results show that DPoS can not only achieve close-to-offline-optimal performance, but also have low algorithmic overheads.
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