An Optimization-enhanced MANO for Energy-efficient 5G Networks

5G network nodes, fronthaul and backhaul alike, will have both forwarding and computational capabilities. This makes energy-efficient network management more challenging, as decisions such as activating or deactivating a node impact on both the ability of the network to route traffic and the amount of processing it can perform. To this end, we formulate an optimization problem accounting for the main features of 5G nodes and the traffic they serve, allowing joint decisions about (i) the nodes to activate, (ii) the network functions they run, and (iii) the traffic routing. Our optimization module is integrated within the management and orchestration framework of 5G, thus enabling swift and high-quality decisions. We test our scheme with both a real-world testbed based on OpenStack and OpenDaylight, and a large-scale emulated network whose topology and traffic come from a real-world mobile operator, finding it to consistently outperform state-of-the art alternatives and closely match the optimum

Um arcabouço holístico para a execução de funções virtualizadas de rede : arquitetura, gerenciamento e aplicações

Orientador: Elias Procópio Duarte JúniorTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 16/12/2022Inclui referênciasÁrea de concentração: Ciência da ComputaçãoResumo: O paradigma de Virtualização de Funções de Rede (Network Function Virtualization - NFV) visa desacoplar as funções de rede de hardware dedicado, utilizando tecnologias de virtualização para implementar as funções em software. A arquitetura de referência NFV tem sido amplamente adotada, sendo esta composta por três domínios: Infraestrutura Virtualizada (Virtualized Infrastructure - VI), Gerenciamento e Orquestração de NFV (NFV Management and Orchestration - NFV-MANO), além das próprias Funções Virtualizadas de Rede (Virtualized Network Functions - VNF). Já serviços virtualizados são definidos como composições de funções virtualizadas, organizados através de topologias que estabelecem o fluxo de processamento do tráfego de rede. Apesar do grande potencial do paradigma NFV, ainda existem importantes desafios para garantir que seja amplamente adotado nas infraestruturas modernas de telecomunicação. Esta Tese tem como objetivo principal contribuir para a gerência do ciclo de vida de funções e serviços virtualizados de rede. Neste sentido assume-se a premissa de que é essencial garantir previsibilidade operacional e padronização dos elementos que atuam no domínio de VNF, i.e., das plataformas de execução de VNF e dos Sistemas de Gerenciamento de Elemento (Element Management System - EMS). São propostas na Tese arquiteturas que descrevem um modelo de execução e gerenciamento padronizado tanto para plataformas de execução de VNF, como para o EMS, compatíveis com a arquitetura de referência NFV-MANO. As arquiteturas propostas preveem protocolos, interfaces de comunicação e orientações de integração com demais elementos e sistemas NFV. Ambas as arquiteturas foram implementadas e estão disponíveis como software livre: a plataforma COVEN de execução de VNF e o EMS HoLMES. Resultados de avaliação experimental e estudos de caso são apresentados e discutidos. Também, uma investigação do impacto das arquiteturas propostas em três contextos distintos é então introduzida. Inicialmente, é explorado seu impacto como facilitador para a gerência de NFV. Em seguida, o foco fica no compartilhamento de instâncias de funções e serviços de rede, e na emulação NFV. A Tese apresenta também contribuições no contexto de serviços virtualizados de rede. Em particular, o mapeamento multidomínio consiste na implantação de serviços em infraestruturas de virtualização distribuídas. Assim, foi proposta uma estratégia de mapeamento baseado em heurísticas genéticas, denominada GeSeMa. A estratégia permite que seus operadores definam diferentes critérios de avaliação e otimização em busca de mapeamentos de serviço adequados às suas necessidades específicas. Neste sentido, o GeSeMa representa um avanço do estado da arte, pois outras soluções operam de maneira monolítica, não permitindo que operadores e gerentes de rede personalizem o esquema de otimização adotado (e.g., métricas a serem avaliadas, pesos a serem considerados e restrições de mapeamento relacionadas a cada serviço em particular). A solução foi testada em múltiplos estudos de caso e os resultados demonstram sua aplicabilidade e ?exibilidade ao lidar com diferentes cenários de otimização de mapeamentos multidomínio. Finalmente, outras duas contribuições da Tese relacionadas ao paradigma NFV são apresentadas como apêndices, nos contextos de tolerância a falhas e engenharia de tráfego.Abstract: The Network Function Virtualization (NFV) paradigm aims to decouple network functions from dedicated hardware, employing virtualization technologies to implement functions in software. The NFV reference architecture has been widely adopted. This architecture, in turn, is composed of three domains: Virtualized Infrastructure (VI), NFV Management and Orchestration (NFV-MANO), and Virtualized Network Functions (VNF). Compositions of virtualized functions define virtualized services, organized as topologies through which network trafic is steered. Despite the extraordinary potential of the NFV paradigm, there are still relevant challenges to ensure its wide adoption by modern telecommunication infrastructures. The main objective of this Thesis is to contribute to the life cycle management of virtualized network functions and services. In this context, we consider it essential to guarantee the operational predictability and organization of the elements of the VNF domain, i.e., VNF execution platforms, and the Element Management System (EMS). Thus, this Thesis presents architectures that describe a standardized execution and management model for both VNF execution platforms and EMS. The proposed architectures are compliant with the NFV-MANO reference architecture and provide protocols, communication interfaces, and guidelines for their integration with other NFV elements and systems. Both architectures have been implemented and are available as open-source software: the COVEN VNF execution platform and the HoLMES EMS. Experimental evaluation results and case studies are presented and discussed. An investigation of the impact of the proposed architectures in three di?erent contexts is also introduced. First, we explore the opportunities regarding the proposed architectures as enablers for NFV management. Then, the focus goes to sharing instances of network functions and services and NFV emulation. In addition, the Thesis presents contributions in the context of virtualized network services. Multidomain mapping consists of deploying services on distributed virtualization infrastructures. A mapping strategy based on genetic heuristics, called GeSeMa, was proposed. GeSeMa enables its operators to define multiple optimization criteria for searching candidate service mappings tailored to their specific requirements. The proposed strategy represents a state-of-the-art advance, as other solutions operate in a monolithic manner: they do not allow operators and network managers to customize the adopted evaluation setup (e.g., metrics to be evaluated, weights to be considered, and mapping constraints related to each specific service). We tested GeSeMa in multiple case studies; the results demonstrate its applicability and flexibility in dealing with different multidomain mapping optimization scenarios. Finally, two other contributions related to the NFV paradigm, approaching fault tolerance and trafic engineering, are presented as appendices

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

Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2019-20 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists

Efficient radio resource management for the fifth generation slice networks

It is predicted that the IMT-2020 (5G network) will meet increasing user demands and, hence, it is therefore, expected to be as flexible as possible. The relevant standardisation bodies and academia have accepted the critical role of network slicing in the implementation of the 5G network. The network slicing paradigm allows the physical infrastructure and resources of the mobile network to be “sliced” into logical networks, which are operated by different entities, and then engineered to address the specific requirements of different verticals, business models, and individual subscribers. Network slicing offers propitious solutions to the flexibility requirements of the 5G network. The attributes and characteristics of network slicing support the multi-tenancy paradigm, which is predicted to drastically reduce the operational expenditure (OPEX) and capital expenditure (CAPEX) of mobile network operators. Furthermore, network slices enable mobile virtual network operators to compete with one another using the same physical networks but customising their slices and network operation according to their market segment's characteristics and requirements. However, owing to scarce radio resources, the dynamic characteristics of the wireless links, and its capacity, implementing network slicing at the base stations and the access network xix becomes an uphill task. Moreover, an unplanned 5G slice network deployment results in technical challenges such as unfairness in radio resource allocation, poor quality of service provisioning, network profit maximisation challenges, and rises in energy consumption in a bid to meet QoS specifications. Therefore, there is a need to develop efficient radio resource management algorithms that address the above mentioned technical challenges. The core aim of this research is to develop and evaluate efficient radio resource management algorithms and schemes that will be implemented in 5G slice networks to guarantee the QoS of users in terms of throughput and latency while ensuring that 5G slice networks are energy efficient and economically profitable. This thesis mainly addresses key challenges relating to efficient radio resource management. First, a particle swarm-intelligent profit-aware resource allocation scheme for a 5G slice network is proposed to prioritise the profitability of the network while at the same time ensuring that the QoS requirements of slice users are not compromised. It is observed that the proposed new radio swarm-intelligent profit-aware resource allocation (NR-SiRARE) scheme outperforms the LTE-OFDMA swarm-intelligent profit-aware resource (LO-SiRARE) scheme. However, the network profit for the NR-SiRARE is greatly affected by significant degradation of the path loss associated with millimetre waves. Second, this thesis examines the resource allocation challenge in a multi-tenant multi-slice multi-tier heterogeneous network. To maximise the total utility of a multi-tenant multislice multi-tier heterogeneous network, a latency-aware dynamic resource allocation problem is formulated as an optimisation problem. Via the hierarchical decomposition method for heterogeneous networks, the formulated optimisation problem is transformed to reduce the computational complexities of the proposed solutions. Furthermore, a genetic algorithmbased latency-aware resource allocation scheme is proposed to solve the maximum utility problem by considering related constraints. It is observed that GI-LARE scheme outperforms the static slicing (SS) and an optimal resource allocation (ORA) schemes. Moreover, the GI-LARE appears to be near optimal when compared with an exact solution based on spatial branch and bound. Third, this thesis addresses a distributed resource allocation problem in a multi-slice multitier multi-domain network with different players. A three-level hierarchical business model comprising InPs, MVNOs, and service providers (SP) is examined. The radio resource allocation problem is formulated as a maximum utility optimisation problem. A multi-tier multi-domain slice user matching game and a distributed backtracking multi-player multidomain games schemes are proposed to solve the maximum utility optimisation problem. The distributed backtracking scheme is based on the Fisher Market and Auction theory principles. The proposed multi-tier multi-domain scheme outperforms the GI-LARE and the SS schemes. This is attributed to the availability of resources from other InPs and MVNOs; and the flexibility associated with a multi-domain network. Lastly, an energy-efficient resource allocation problem for 5G slice networks in a highly dense heterogeneous environment is investigated. A mathematical formulation of energy-efficient resource allocation in 5G slice networks is developed as a mixed-integer linear fractional optimisation problem (MILFP). The method adopts hierarchical decomposition techniques to reduce complexities. Furthermore, the slice user association, QoS for different slice use cases, an adapted water filling algorithm, and stochastic geometry tools are employed to xxi model the global energy efficiency (GEE) of the 5G slice network. Besides, neither stochastic geometry nor a three-level hierarchical business model schemes have been employed to model the global energy efficiency of the 5G slice network in the literature, making it the first time such method will be applied to 5G slice network. With rigorous numerical simulations based on Monte-Carlo numerical simulation technique, the performance of the proposed algorithms and schemes was evaluated to show their adaptability, efficiency and robustness for a 5G slice network