Hierarchical Resource Allocation in Multi-Service Wireless Networks With Wireless Network Virtualization
| Autor: | Xiaofeng Tao, Xuefei Zhang, Sijia Jia, Yan Han |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Optimization problem
Computer Networks and Communications Wireless network business.industry Network packet Computer science Distributed computing Quality of service Aerospace Engineering 020302 automobile design & engineering Lyapunov optimization 02 engineering and technology Scheduling (computing) 0203 mechanical engineering Automotive Engineering Wireless Resource allocation Stochastic optimization Resource management Data as a service Electrical and Electronic Engineering business |
| Zdroj: | IEEE Transactions on Vehicular Technology. 69:11811-11827 |
| ISSN: | 1939-9359 0018-9545 |
| DOI: | 10.1109/tvt.2020.3019217 |
| Popis: | To balance the contradiction between the rapid growth of data service demands and the limited spectrum resources, wireless network virtualization (WNV) has been proposed as a promising technology by isolating and sharing wireless resources among different virtual networks in the future wireless networks. In this paper, a two-dimension-time-scale hierarchical resource allocation scheme is proposed in the multi-service wireless virtualized network, which consists of three 5G generic scenarios. The resource slicing problem is decomposed into two time scales including large time period for inter-slice resource pre-allocation and small time slot for intra-slice resource scheduling. In large time period, the inter-slice resource pre-allocation problem is formulated as a multi-objective optimization problem (MOOP) by modeling the packets arriving and serving process of each slice as a queueing system. While in small time slot, the resource block (RB) and power scheduling in each slice is formulated as a stochastic optimization problem considering dynamic traffic arrivals and time-varying channel conditions, which is aimed at optimizing the overall performance subject to various quality of service (QoS) requirements such as network stability, delay, reliability, transmission rate and power constraints. The stochastic optimization problem can be transformed into a delay-aware optimization problem by applying Lyapunov optimization technique, and be solved by the proposed algorithm consisting of a heuristic algorithm and a concave optimization algorithm. The simulation results show that the proposed schemes are close to the optimal solution with a lower complexity, which can also achieve a performance-delay tradeoff related to the control factor. |
| Databáze: | OpenAIRE |
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