Dynamic network slicing for multitenant heterogeneous cloud radio access networks

Multitenant cellular network slicing has been gaining huge interest recently. However, it is not well-explored under the heterogeneous cloud radio access network (H-CRAN) architecture. This paper proposes a dynamic network slicing scheme for multitenant H-CRANs, which takes into account tenants' priority, baseband resources, fronthaul and backhaul capacities, quality of service (QoS) and interference. The framework of the network slicing scheme consists of an upper-level, which manages admission control, user association and baseband resource allocation; and a lower-level, which performs radio resource allocation among users. Simulation results show that the proposed scheme can achieve a higher network throughput, fairness and QoS performance compared to several baseline schemes

Rate-delay analysis of radio access network slices

Based on wireless network virtualization, radio access network (RAN) slicing is developed to provide services for the different users' requirements. Moreover, the users' sum data rate and delay are two significant metrics to guarantee quality of services. In this paper, we first establish an optimization problem to maximize the downlink sum rate while guaranteeing users' delay for RAN slices, where the base stations and user equipments are randomly distributed. Then we analyze the performance tradeoff between the sum rate maximization and delay tolerance. With the aid of Lyapunov optimization, the upper bounds of the achievable rate and delay are derived, through which the existence of tradeoff in performance is obvious and verified by numerical results

The Network Slicing and Performance Analysis of 6G Networks using Machine Learning

6G technology is designed to provide users with faster and more reliable data  transfer as compared to the current 5G technology. 6G is rapidly evolving and provides a large bandwidth, even in underserved areas. This technology is extremely anticipated and is currently booming for its ability to deliver massive network capacity, low latency, and a highly improved user experience. Its scope is immense, and it’s designed to connect everyone and everything in the world. It includes new deployment models and services with extended user capacity. This study proposes a network slicing simulator that uses hardcoded base station coordinates to randomly distribute client locations to help analyse the performance of a particular base station architecture. When a client wants to locate the closest base station, it queries the simulator, which stores base station coordinates in a K-Dimensional tree. Throughout the simulation, the user follows a pattern that continues until the time limit is achieved. It gauges multiple statistics such as client connection ratio, client count per second, Client count per slice, latency, and the new location of the client. The K-D tree handover algorithm proposed here allows the user to connect to the nearest base stations after fulfilling the required criteria. This algorithm ensures the quality requirements and decides among the base stations the user connects to

Multitenant Containers as a Service (CaaS) for Clouds and Edge Clouds

Cloud computing, offering on-demand access to computing resources through the Internet and the pay-as-you-go model, has marked the last decade with its three main service models; Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The lightweight nature of containers compared to virtual machines has led to the rapid uptake of another in recent years, called Containers as a Service (CaaS), which falls between IaaS and PaaS regarding control abstraction. However, when CaaS is offered to multiple independent users, or tenants, a multi-instance approach is used, in which each tenant receives its own separate cluster, which reimposes significant overhead due to employing virtual machines for isolation. If CaaS is to be offered not just at the cloud, but also at the edge cloud, where resources are limited, another solution is required. We introduce a native CaaS multitenancy framework, meaning that tenants share a cluster, which is more efficient than the one tenant per cluster model. Whenever there are shared resources, isolation of multitenant workloads is an issue. Such workloads can be isolated by Kata Containers today. Besides, our framework esteems the application requirements that compel complete isolation and a fully customized environment. Node-level slicing empowers tenants to programmatically reserve isolated subclusters where they can choose the container runtime that suits application needs. The framework is publicly available as liberally-licensed, free, open-source software that extends Kubernetes, the de facto standard container orchestration system. It is in production use within the EdgeNet testbed for researchers

On the Feasibility of 5G Slice Resource Allocation With Spectral Efficiency: A Probabilistic Characterization

An important concern that 5G networks face is supporting a wide range of services and use cases with heterogeneous requirements. Radio access network (RAN) slices, understood as isolated virtual networks that share a common infrastructure, are a possible answer to this very demanding scenario and enable virtual operators to provide differentiated services over independent logical entities. This article addresses the feasibility of forming 5G slices, answering the question of whether the available capacity (resources) is sufficient to satisfy slice requirements. As spectral efficiency is one of the key metrics in 5G networks, we introduce the minislot-based slicing allocation (MISA) model, a novel 5G slice resource allocation approach that combines the utilization of both complete slots (or physical resource blocks) and mini-slots with the adequate physical layer design and service requirement constraints. We advocate for a probabilistic characterization that allows to estimate feasibility and characterize the behavior of the constraints, while an exhaustive search is very computationally demanding and the methods to check feasibility provide no information on the constraints. In such a characterization, the concept of phase transition allows for the identification of a clear frontier between the feasible and infeasible regions. Our method relies on an adaptation of the Wang-Landau algorithm to determine the existence of, at least, one solution to the problem. The conducted simulations show a significant improvement in spectral efficiency and feasibility of the MISA approach compared to the slot-based formulation, the identification of the phase transition, and valuable results to characterize the satisfiability of the constraints.The work of J. J. Escudero-Garzás was supported in part by the Spanish National Project TERESA-ADA (MINECO/AEI/FEDER, UE) under Grant TEC2017-90093-C3-2-R, and in part by the National Spectrum Consortium, USA, under Project NSC-16-0140

Cooperative cognitive network slicing virtualization for smart IoT applications

This paper proposes the cooperative cognitive net-work slicing virtualization solution for smart Internet of things (IoT) applications. To this end, we deploy virtualized small base stations (vSBSs) in SDR devices that offer network-slicing virtualization option. The proposed virtualized solution relies on Fed4Fire wireless experimental platform. In particular, we assume that multiple IoT devices can have access to different vSBSs, which coordinate their resources in a cooperative manner using machine learning (ML). To this end, a proactive resource management is deployed in the unlicensed band, where a cooperative solution is facilitated using the licensed band. The cooperative network slicing is managed and orchestrated using small cell virtualization offered by the Fed4Fire. Experimental trials are carried out for certain number of users and results are obtained that highlight the benefit of employing cooperative cognitive network slicing in future virtualized wireless networks

How should I slice my network? A multi-service empirical evaluation of resource sharing efficiency

Proceeding of: MobiCom '18: The 24th Annual International Conference on Mobile Computing and Networking, New Delhi, India, October 29 - November 2, 2018By providing especially tailored instances of a virtual network,network slicing allows for a strong specialization of the offered services on the same shared infrastructure. Network slicing has profound implications on resource management, as it entails an inherent trade-off between: (i) the need for fully dedicated resources to support service customization, and (ii) the dynamic resource sharing among services to increase resource efficiency and cost-effectiveness of the system. In this paper, we provide a first investigation of this trade-off via an empirical study of resource management efficiency in network slicing. Building on substantial measurement data collected in an operational mobile network (i) we quantify the efficiency gap introduced by non-reconfigurable allocation strategies of different kinds of resources, from radio access to the core of the network, and (ii) we quantify the advantages of their dynamic orchestration at different timescales. Our results provide insights on the achievable efficiency of network slicing architectures, their dimensioning, and their interplay with resource management algorithms.We would like to thank the shepherd and reviewers for their valuable comments and feedback. The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (grant agreement no. 761445), and the work of NEC Europe Ltd. was supported by the H2020 5GTransformer project (grant agreement no. 761536)