Abstract
Wireless network virtualization (WNV) provides a novel paradigm shift in the fifth-generation (5G) system, which enables to utilize network resources more efficiently. In this paper, by jointly considering cache space and time-frequency resource allocation in wireless virtualized networks, we first formulate an optimization programming to investigate the minimization problem of network overheads while satisfying the quality of service (QoS) requirements of each virtual network on overflow probability. Then, with diverse demands of virtual networks for different kinds of resources taken into consideration, an online adaptive virtual resource allocation algorithm with multiple time-scales based on auto regressive moving average (ARMA) prediction method is proposed to solve the formulation, which could eliminate the irrationalities existed in traditional approaches caused by the uncertainty of traffic and information feedback delay. More specifically, in the proposed resource scheduling mechanism with multiple time-scales, on the one hand, a reservation strategy of cache space is developed according to the ARMA prediction information under long time-scales. On the other hand, virtual networks are sorted by the overflow probabilities derived by the large-deviation principle and dynamic time-frequency resource scheduling under short time-scales. Simulation results reveal that our proposal can provide tangible gains in reducing the bit loss rate and improving the utilization of physical resources.
Highlights
With the rapid development of intelligent terminals, the flourish of diversified applications which have different demands for delay, reliability and throughput, has brought great challenges to the existing network [1]
Consider that the stochasticity of network states and delay caused by information feedback may lead to unreasonable virtual resource allocation in the networks, in this paper, we investigate the joint cache space and time-frequency resource blocks (RBs) allocation problem in dynamic wireless virtualized networks with large-deviation principle and auto regressive moving average (ARMA) prediction, propose an adaptive virtual resource allocation algorithm to solve the formulation
Leveraging the large-deviation principle and the ARMA prediction method, we propose an online adaptive virtual resource allocation algorithm with multiple time-scales to solve the formulation while taking diverse demands of virtual networks for different types of resources into consideration, which could eliminate the unreasonableness existed in traditional approaches
Summary
With the rapid development of intelligent terminals, the flourish of diversified applications which have different demands for delay, reliability and throughput, has brought great challenges to the existing network [1]. Consider that the stochasticity of network states and delay caused by information feedback may lead to unreasonable virtual resource allocation in the networks, in this paper, we investigate the joint cache space and time-frequency resource blocks (RBs) allocation problem in dynamic wireless virtualized networks with large-deviation principle and auto regressive moving average (ARMA) prediction, propose an adaptive virtual resource allocation algorithm to solve the formulation. The main work is to design a dynamic allocation strategy of cache space and time-frequency RBs for the resource management entity in the virtual network management platform, with the purpose of decreasing the physical resource leasing cost and simultaneously guaranteeing the QoS requirements of each virtual network
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