Dynamic Resource Trading in Sliced Mobile Networks

Abstract

Expanding the market of mobile network services and defining solutions that are cost efficient are the key challenges for next generation mobile networks. Network slicing is commonly considered to be the main instrument to exploit the flexibility of the new radio interface and core network functions. It targets splitting resources among services with different requirements and tailoring system parameters according to their needs. Regulation authorities also recognize network slicing as a way of opening the market to new players who can specialize in providing new mobile services acting as "tenants" of the slices. Resources can also be distributed between infrastructure providers and tenants so that they meet the requirements of the services offered. In this paper, we propose a model for dynamic trading of mobile network resources in a market that enables automatic optimization of technical parameters and of economic prices according to high level policies defined by the tenants. We introduce a mathematical formulation for the problems of resource allocation and price definition and show how the proposed approach can cope with quite diverse service scenarios presenting a large set of numerical results.

Reinforcement Learning Based Resource Management for Network Slicing

“…In [12], Xie et al proposed a dynamic resource pooling and trading mechanism in network virtualization using Stackelberg game to achieve more efficient utilization of network resources. In [13,14], Akguel et al proposed an algorithm for trading resources among tenants. However, their approach involves a high computational cost that can make it impractical for real-scenarios.…”

Anticipatory Resource Allocation and Trading in a Sliced Network

“…Extending our previous work [9], we investigate the dynamic negotiation platform provided in Fig. 1.…”

“…error-free) prediction, is not robust to errors, which have direct impact on the slice configuration. Therefore, since guaranteeing high prediction accuracy in real time is quite challenging, we tackle the problem by splitting our original formulation into two subproblems, namely to P 1 and P 2 , as in our previous work [9]. However, our aim with the two-step algorithm in this case is different from what we had in [9].…”

“…Therefore, they cannot provide the efficiency and flexibility required by a dynamic wireless resource market. With these objectives in mind, we proposed in [9] a dynamic slicing and resource trading platform. In this platform, the tenants determine a set of high-level SLA policies in line with their business strategies, which are automatically translated into sharing parameters, to be used for the real time resource allocation.…”

“…Despite the level of autonomy and flexibility that we achieved in [9], high-level renegotiations still occur in a reactive manner, that results in tenants' taking business risks, while at the same time it limits the efficiency of resource allocation for the infrastructure provider. Therefore, in this work, we focus on exploiting anticipatory information [10] during the negotiation period to maximize the efficiency of network slicing.…”