A Biological Model for Resource Allocation and User Dynamics in Virtualized HetNet

Abstract

Virtualization technology is considered an effective measure to enhance resource utilization and interference management via radio resource abstraction in heterogeneous networks (HetNet). The critical challenge in wireless virtualization is virtual resource allocation on which substantial works have been done. However, most existing researches on virtual resource allocation focus on improving total utility. Different from the existing works, we investigate the dynamic-aware virtual radio resource allocation in virtualization based HetNet considering utility and fairness. A virtual radio resource management framework is proposed, where the radio resources of different physical networks are virtualized into a virtual resource pool and mobile virtual network operators (MVNOs) compete for virtual resources from the pool to provide service to users. A virtual radio resource allocation algorithm based on biological model is developed, considering system utility, fairness, and dynamics. Simulation results are provided to verify that the proposed virtual resource allocation algorithm not only converges within a few iterations, but also achieves a better trade-off between total utility and fairness than existing algorithm. Besides, it can also be utilized to analyze the population dynamics of system.

Multi-tier multi-tenant network slicing: A multi-domain games approach

“…In [24], the authors proposed a dynamic virtual resource allocation framework premised on the widely known biological population model called Lotka-Volterra [46]. The virtual resources of the multiple InPs are centralised and then allocated to MVNOs.…”

“…The virtual resources of the multiple InPs are centralised and then allocated to MVNOs. Similar to [24], the authors in [25,26,27] proposed dynamic resource allocation schemes which are centrally controlled in heterogeneous networks.…”

“…6 -Multi-tier multi-domain slice user matching game. and its associated constraints given in (24)a-( 24)d. To this end, we obtain optimal value of 𝒴 𝑖,𝑛 by adopting a recursive distributed backtracking technique [80,81] for respective SPs. The recursive distributed backtracking relies on partial or incomplete information, which is a primary feature in the hierarchical layers of the M-TTSD network [82,83].…”

“…To this end, the slice user ratio lies between 0 and 1, hence, it is a positive value. Moreover, the IC for MVNOs is guaranteed in constraint(24)d. To this end, the sum bids 𝛿 𝑛,𝑑 of SPs associated with MVNO 𝑑 must exceed the sum of the bids 𝜓 𝑑𝑘 placed by MVNO 𝑑 for the resources of InPs associated with it.…”