Multi-tenant radio access network slicing: statistical multiplexing of spatial loads

This paper addresses the slicing of radio access network resources by multiple tenants, e.g., virtual wireless operators and service providers. We consider a criterion for dynamic resource allocation amongst tenants, based on a weighted proportionally fair objective, which achieves desirable fairness/protection across the network slices of the different tenants and their associated users. Several key properties are established, including: the Pareto-optimality of user association to base stations, the fair allocation of base stations' resources, and the gains resulting from dynamic resource sharing across slices, both in terms of utility gains and capacity savings. We then address algorithmic and practical challenges in realizing the proposed criterion. We show that the objective is NP-hard, making an exact solution impractical, and design a distributed semi-online algorithm, which meets performance guarantees in equilibrium and can be shown to quickly converge to a region around the equilibrium point. Building on this algorithm, we devise a practical approach with limited computational information and handoff overheads. We use detailed simulations to show that our approach is indeed near-optimal and provides substantial gains both to tenants (in terms of capacity savings) and end users (in terms of improved performance).The work of P. Caballero and G. de Veciana was supported by NSF Award CNS-1343383, the work of A. Banchs and X. Costa-Pérez by the H2020-ICT-2014-2 5G NORMA (Grant Agreement No. 671584), and the work of A. Banchs was also partially supported by the Spanish project DRONEXT (Grant Agreement TEC2014-58964-C2-1-R)

Network slicing for guaranteed rate services: admission control and resource allocation games

Technologies that enable network slicing are expected to be a key component of next generation mobile networks. Their promise lies in enabling tenants (such as mobile operators and/or services) to reap the cost and performance benefits of sharing resources while retaining the ability to customize their own allocations. When employing dynamic sharing mechanisms, tenants may exhibit strategic behavior, optimizing their choices in response to those of other tenants. This paper analyzes dynamic sharing in network slicing when tenants support inelastic users with minimum rate requirements. We propose a NEtwork Slicing (NES) framework combining: 1) admission control; 2) resource allocation; and 3) user dropping. We model the network slicing system with admitted users as a NES game; this is a new class of game where the inelastic nature of the traffic may lead to dropping users whose requirements cannot be met. We show that, as long as admission control guarantees that slices can satisfy the rate requirements of all their users, this game possesses a Nash equilibrium. Admission control policies (a conservative and an aggressive one) are considered, along with a resource allocation scheme and a user dropping algorithm, geared at maintaining the system in Nash equilibria. We analyze our NES framework's performance in equilibrium, showing that it achieves the same or better utility than static resource partitioning, and bound the difference between NES and the socially optimal performance. Simulation results confirm the effectiveness of the proposed approach.The work of University of Texas at Austin was supported in part by a gift from Cisco. The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the 5GCity project of the Spanish Ministry of Economy and Competitiveness (TEC2016-76795-C6-3-R). The work of NEC Europe Ltd. was supported by the H2020 5G-Transformer project (Grant agreement no. 761536)

Statistical Multiplexing and Traffic Shaping Games for Network Slicing

Next generation wireless architectures are expected to enable slices of shared wireless infrastructure which are customized to specific mobile operators/services. Given infrastructure costs and the stochastic nature of mobile services' spatial loads, it is highly desirable to achieve efficient statistical multiplexing amongst such slices. We study a simple dynamic resource sharing policy which allocates a 'share' of a pool of (distributed) resources to each slice-Share Constrained Proportionally Fair (SCPF). We give a characterization of SCPF's performance gains over static slicing and general processor sharing. We show that higher gains are obtained when a slice's spatial load is more 'imbalanced' than, and/or 'orthogonal' to, the aggregate network load, and that the overall gain across slices is positive. We then address the associated dimensioning problem. Under SCPF, traditional network dimensioning translates to a coupled share dimensioning problem, which characterizes the existence of a feasible share allocation given slices' expected loads and performance requirements. We provide a solution to robust share dimensioning for SCPF-based network slicing. Slices may wish to unilaterally manage their users' performance via admission control which maximizes their carried loads subject to performance requirements. We show this can be modeled as a 'traffic shaping' game with an achievable Nash equilibrium. Under high loads, the equilibrium is explicitly characterized, as are the gains in the carried load under SCPF vs. static slicing. Detailed simulations of a wireless infrastructure supporting multiple slices with heterogeneous mobile loads show the fidelity of our models and range of validity of our high load equilibrium analysis

Constrained Network Slicing Games: Achieving service guarantees and network efficiency

Network slicing is a key capability for next generation mobile networks. It enables one to cost effectively customize logical networks over a shared infrastructure. A critical component of network slicing is resource allocation, which needs to ensure that slices receive the resources needed to support their mobiles/services while optimizing network efficiency. In this paper, we propose a novel approach to slice-based resource allocation named Guaranteed seRvice Efficient nETwork slicing (GREET). The underlying concept is to set up a constrained resource allocation game, where (i) slices unilaterally optimize their allocations to best meet their (dynamic) customer loads, while (ii) constraints are imposed to guarantee that, if they wish so, slices receive a pre-agreed share of the network resources. The resulting game is a variation of the well-known Fisher market, where slices are provided a budget to contend for network resources (as in a traditional Fisher market), but (unlike a Fisher market) prices are constrained for some resources to provide the desired guarantees. In this way, GREET combines the advantages of a share-based approach (high efficiency by flexible sharing) and reservation-based ones (which provide guarantees by assigning a fixed amount of resources). We characterize the Nash equilibrium, best response dynamics, and propose a practical slice strategy with provable convergence properties. Extensive simulations exhibit substantial improvements over network slicing state-of-the-art benchmarks

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis

Network slicing games: enabling customization in multi-tenant mobile networks

Network slicing to enable resource sharing among multiple tenants-network operators and/or services-is considered as a key functionality for next generation mobile networks. This paper provides an analysis of a well-known model for resource sharing, the share-constrained proportional allocation mechanism, to realize network slicing. This mechanism enables tenants to reap the performance benefits of sharing, while retaining the ability to customize their own users' allocation. This results in a network slicing game in which each tenant reacts to the user allocations of the other tenants so as to maximize its own utility. We show that, for elastic traffic, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a tenant always achieves the same or better performance than that of a static partitioning of resources, thus providing the same level of protection as static partitioning. We further analyze the efficiency and fairness of the resulting allocations, providing tight bounds for the price of anarchy and envy-freeness. Our analysis and extensive simulation results confirm that the mechanism provides a comprehensive practical solution to realize network slicing. Our theoretical results also fills a gap in the analysis of this resource allocation model under strategic players.The work of P. Caballero and G. De Veciana was supported in part by Cisco through a gift. The work of A. Banchs was supported in part by the H2020 5G-MoNArch Project under Grant 761445 and in part by the 5GCity Project of the Spanish Ministry of Economy and Competitiveness under Grant TEC2016-76795-C6-3-R