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

NSBchain: A Secure Blockchain Framework for Network Slicing Brokerage

With the advent of revolutionary technologies, such as virtualization and softwarization, a novel concept for 5G networks and beyond has been unveiled: Network Slicing. Initially driven by the research community, standardization bodies as 3GPP have embraced it as a promising solution to revolutionize the traditional mobile telecommunication market by enabling new business models opportunities. Network Slicing is envisioned to open up the telecom market to new players such as Industry Verticals, e.g. automotive, smart factories, e-health, etc. Given the large number of potential new business players, dubbed as network tenants, novel solutions are required to accommodate their needs in a cost-efficient and secure manner. In this paper, we propose NSBchain, a novel network slicing brokering (NSB) solution, which leverages on the widely adopted Blockchain technology to address the new business models needs beyond traditional network sharing agreements. NSBchain defines a new entity, the Intermediate Broker (IB), which enables Infrastructure Providers (InPs) to allocate network resources to IBs through smart contracts and IBs to assign and re-distribute their resources among tenants in a secure, automated and scalable manner. We conducted an extensive performance evaluation by means of an open-source blockchain platform that proves the feasibility of our proposed framework considering a large number of tenants and two different consensus algorithms

OROS: onlin operation and orchestration of collaborative robots using 5G

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe 5G mobile networks extend the capability for supporting collaborative robot operations in outdoor scenarios. However, the restricted battery life of robots still poses a major obstacle to their effective implementation and utilization in real scenarios. One of the most challenging situations is the execution of mission-critical tasks that require the use of various onboard sensors to perform simultaneous localization and mapping (SLAM) of unexplored environments. Given the time-sensitive nature of these tasks, completing them in the shortest possible time is of the highest importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the intelligence of the robot operation through joint orchestration of Robot Operating System (ROS) and 5G resources for energysaving goals, addressing the problem from both offline and online manners. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times as well as overall energy consumption of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. We validate our 5G-enabled collaborative framework by means of Matlab/Simulink, ROS software and Gazebo simulator. Our results show an improvement between 3.65in exploration task by exploiting 5G orchestration features for battery savings when using 3 robots.Peer ReviewedPostprint (author's final draft

Overbooking Network Slices through Yield-driven End-to-End Orchestration

Proceeding of: 14th International Conference on emerging Networking EXperiments and Technologies (CoNEXT '18)Network slicing allows mobile operators to offer, via proper abstractions, mobile infrastructure (radio, networking, computing) to vertical sectors traditionally alien to the telco industry (e.g., automotive, health, construction). Owning to similar business nature, in this paper we adopt yield management models successful in other sectors (e.g. airlines, hotels, etc.) and so we explore the concept of slice overbooking to maximize the revenue of mobile operators. The main contribution of this paper is threefold. First, we design a hierarchical control plane to manage the orchestration of slices end-to-end, including radio access, transport network, and distributed computing infrastructure. Second, we cast the orchestration problem as a stochastic yield management problem and propose two algorithms to solve it: an optimal Benders decomposition method and a suboptimal heuristic that expedites solutions. Third, we implement an experimental proof-of-concept and assess our approach both experimentally and via simulations with topologies from three real operators and a wide set of realistic scenarios. Our performance evaluation shows that slice overbooking can provide up to 3x revenue gains in realistic scenarios with minimal footprint on service-level agreements (SLAs).This work was supported in part by the H2020 5G-Transformer Project under Grant 761536 and in part by H2020-MSCA-ITN-2015 5G-Aura Project under Grant 675806

Latency-driven Network Slices Orchestration

This paper has been presented at: IEEE Conference on Computer Communications Workshops ( INFOCOM'19 )The novel concept of network slicing is envisioned to allow service providers to open their infrastructure to vertical industries traditionally alien to mobile networks, such as automotive, health or factories. In this way multiple vertical services can be delivered over the same physical facilities by means of advanced network virtualization techniques. However, the vertical service requirements heterogeneity (e.g., high throughput, low latency, high reliability) calls for novel orchestration solutions able to manage end-to-end network slice resources across different domains while satisfying stringent service level agreements. In this demonstration we will show a novel orchestration solution able to handle one of the most stringent requirements: end-to-end latency. Our testbed-evolution of the work presented in [1]-implements all the resource brokerage schemes and allocation operations necessary to complete the life-cycle management of network slices. In addition, the novel overbooking concept is applied to pursue the overall revenue maximization when admitting network slices. Finally, an advanced network slicing monitoring system will be provided as a user-friendly dashboard allowing users to interact with the proposed solution.This work was supported by the H2020 5G-Transformer Project under Grant 761536 and by the H2020-MSCA-ITN-2015 5G-AURA Project under Grant 675806

Overbooking Network Slices End-to-End: Implementation and Demonstration

This paper has been presented at: ACM SIGCOMM 2018 Conference on Posters and DemosThe novel network slicing paradigm allows service providers to open their infrastructure to new business players such as vertical industries. In this demo, we showcase the benefits of our proposed end-to-end network slicing orchestration solution that blends together i) an admission control engine able to handle heterogeneous network slice requests, ii) a resource allocation solution across multiple network domains: radio access, edge, transport and core networks and iii) a monitoring, forecasting and dynamic configuration solution that maximizes the statistical multiplexing of network slices resources. Our orchestration solution is operated through a dashboard that allows requesting network slices on-demand, monitors their performance once deployed and displays the achieved multiplexing gain through overbooking

On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining specialized clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed Federated DRL approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllers.Comment: 15 pages, 15 Figures, accepted for publication at IEEE TV

On the specialization of FDRL agents for scalable and distributed 6G RAN slicing orchestration

©2022 IEEE. Reprinted, with permission, from Rezazadeh, F., Zanzi, L., Devoti, F. et.al. On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration. IEEE Transactions on vehicular technology (Online) October 2022Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design a hierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, defining specialized clusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposed Federated DRL approach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllersPeer ReviewedPreprin

A Survey on Explainable AI for 6G O-RAN: Architecture, Use Cases, Challenges and Research Directions

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use-cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks.Comment: 33 pages, 13 figure