Network Slicing

Network slicing is emerging as a key enabling technology to support new service needs, business cases, and the evolution of programmable networking. As an end-to-end concept involving network functions in different domains and administrations, network slicing calls for new standardization efforts, design methodologies, and deployment strategies. This chapter aims at addressing the main aspects of network slicing with relevant challenges and practical solutions

Cost Effective Provisioning of 5G Transport Networks: Architectures and Modelling

The next generation of mobile network (5G) has to face a completely new set of requirements coming from novel services. Massive machine type communications, enhanced mobile broadband, ultra reliable low latency communication will be supported by single infrastructure. Mobile network operators are in need of a flexible network capable of supporting services with a wide set of different requirements over the same physical resources, possibly at the same or at a lower cost than today. Centralized radio access network (C-RAN) architecture is a promising solution to improve both network flexibility and scalability. In C-RAN, baseband processing units (BBUs) are decoupled from remote radio units (RRUs) at the antenna sites and are placed in one of few selected locations, called BBU hotels. Thanks to the centralization, more efficient hardware can be employed, advanced radio interference management techniques can be implemented, cooling and power supply units can be shared, and network maintenance is simplified. However, the centralization of BBUs requires high capacity and low latency dedicated links to transport data, known as fronthaul links. This may be expensive and calls for novel deployment strategies to contain the costs. This Ph.D. thesis investigates the cost-efficient and resilient design of C-RAN. Minimization of network equipment as well as reuse of already deployed infrastructure, either based on fiber or copper cables, is investigated and shown to be effective to reduce the overall cost. Moreover, the introduction of wireless devices (e.g., based on free space optic) in fronthaul links is included in the proposed deployment strategies and shown to significantly lower capital expenditure. The adoption of Ethernet-based fronthaul and the introduction of hybrid switches is pursued to further decrease network cost by increasing optical resources usage. Finally, the problem of single BBU hotel failure is addressed and included in the optimal deployment of BBU resources

A Resource Sharing Method for Reliable Slice as a Service Provisioning in 5G Metro Networks

This paper proposes a dynamic slice provisioning analysis in a 5G metro network with reliability guarantees and possible sharing of backup resources. Performance of dedicated (DP) and shared (SP) protection solutions are evaluated with respect to slice resource allocation (i.e., bandwidth and processing units). The main results show a remarkable saving, in terms of slice acceptance rate, by applying SP solutions with respect to conventional DP ones

From IoT to Cloud: Applications and Performance of the MQTT Protocol

A study of the MQTT publish/subscribe protocol with different QoS levels is presented with the aim to extend the Internet of Things (IoT) concept across access, edge and transport networks and reach cloud computing facilities. A simple testbed is set up with related software components to measure the end-to-end delivery latency between the publisher and the subscribers and the impact of the network delay caused by network configurations with different service deployments. In particular, the latency is shown to rise up to more than 7 times the average network delay when the QoS 2 level is applied, thus indicating that its adoption must be carefully considered

Techno-economics of 5G transport deployments

Network densification is a crucial enabler for 5G, requiring the installation of a large number of devices and/or cables for the 5G transport network. This invited paper provides a techno-economic study focusing on adopting microwave and fiber equipment for 5G transport network deployments. Different architectures for low layer split supporting latency critical services are considered

Network Slicing Automation: Challenges and Benefits

Network slicing is a technique widely used in 5G networks where multiple logical networks (i.e., slices) run over a single shared physical infrastructure. Each slice may realize one or multiple services, whose specific requirements are negotiated beforehand and regulated through Service Level Agreements (SLAs).\ua0 In Beyond 5G (B5G) networks it is envisioned that slices should be created, deployed, and managed in an automated fashion (i.e., without human intervention) irrespective of the technological and administrative domains over which a slice may span.\ua0Achieving this vision requires a combination of novel physical layer technologies, artificial intelligence tools, standard interfaces, network function virtualization, and software-defined networking principles. This paper provides an overview of the challenges facing network slicing automation with a focus on transport networks. Results from a selected group of use cases show the benefits of applying conventional optimization tools and machine-learning-based techniques while addressing some slicing design and provisioning problems

Benefits of Pod dimensioning with best-effort resources in bare metal cloud native deployments

Container orchestration platforms automatically adjust resources to evolving traffic conditions. However, these scaling mechanisms are reactive and may lead to service degradation. Traditionally, resource dimensioning has been performed considering guaranteed (or request) resources. Recently, container orchestration platforms included the possibility of allocating idle (or limit) resources for a short time in a best-effort fashion. This paper analyzes the potential of using limit resources as a way to mitigate degradation while reducing the number of allocated request resources. Results show that a 25% CPU reduction can be achieved by relying on limit resources