Contributions to energy-aware demand-response systems using SDN and NFV for fog computing

Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns worldwide that drive the urgent creation of new energy management and consumption schemes. In this regard, by leveraging the massive connectivity provided by emerging communications such as the 5G systems, this thesis proposes a long-term sustainable Demand-Response solution for the adaptive and efficient management of available energy consumption for Internet of Things (IoT) infrastructures, in which energy utilization is optimized based on the available supply. In the proposed approach, energy management focuses on consumer devices (e.g., appliances such as a light bulb or a screen). In this regard, by proposing that each consumer device be part of an IoT infrastructure, it is feasible to control its respective consumption. The proposal includes an architecture that uses Network Functions Virtualization (NFV) and Software Defined Networking technologies as enablers to promote the primary use of energy from renewable sources. Associated with architecture, this thesis presents a novel consumption model conditioned on availability in which consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as the prioritization of the energy supply, workload scheduling using time-shifting capabilities, and quality degradation to decrease- the power demanded by consumers if needed. The adaptive energy management solution is modeled as an Integer Linear Programming, and its complexity has been identified to be NP-Hard. To verify the improvements in energy utilization, an optimal algorithmic solution based on a brute force search has been implemented and evaluated. Because the hardness of the adaptive energy management problem and the non-polynomial growth of its optimal solution, which is limited to energy management for a small number of energy demands (e.g., 10 energy demands) and small values of management mechanisms, several faster suboptimal algorithmic strategies have been proposed and implemented. In this context, at the first stage, we implemented three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs). Then, we incorporated into both the optimal and heuristic strategies a prepartitioning method in which the total set of analyzed services is divided into subsets of smaller size and complexity that are solved iteratively. As a result of the adaptive energy management in this thesis, we present eight strategies, one timal and seven heuristic, that when deployed in communications infrastructures such as the NFV domain, seek the best possible scheduling of demands, which lead to efficient energy utilization. The performance of the algorithmic strategies has been validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and the processing of energy demands. Additionally, the simulation results revealed that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands. This thesis also explores possible application scenarios of both the proposed architecture for adaptive energy management and algorithmic strategies. In this regard, we present some examples, including adaptive energy management in-home systems and 5G networks slicing, energy-aware management solutions for unmanned aerial vehicles, also known as drones, and applicability for the efficient allocation of spectrum in flex-grid optical networks. Finally, this thesis presents open research problems and discusses other application scenarios and future work.El constante aumento del consumo de energía, el agotamiento de los recursos no renovables, el impacto climático asociado con la generación de energía y la capacidad finita de producción de energía son preocupaciones importantes en todo el mundo que impulsan la creación urgente de nuevos esquemas de consumo y gestión de energía. Al aprovechar la conectividad masiva que brindan las comunicaciones emergentes como los sistemas 5G, esta tesis propone una solución de Respuesta a la Demanda sostenible a largo plazo para la gestión adaptativa y eficiente del consumo de energía disponible para las infraestructuras de Internet of Things (IoT), en el que se optimiza la utilización de la energía en función del suministro disponible. En el enfoque propuesto, la gestión de la energía se centra en los dispositivos de consumo (por ejemplo, electrodomésticos). En este sentido, al proponer que cada dispositivo de consumo sea parte de una infraestructura IoT, es factible controlar su respectivo consumo. La propuesta incluye una arquitectura que utiliza tecnologías de Network Functions Virtualization (NFV) y Software Defined Networking como habilitadores para promover el uso principal de energía de fuentes renovables. Asociada a la arquitectura, esta tesis presenta un modelo de consumo condicionado a la disponibilidad en el que los consumidores son parte del proceso de gestión. Para utilizar eficientemente la energía de fuentes renovables y no renovables, se proponen varias estrategias de gestión, como la priorización del suministro de energía, la programación de la carga de trabajo utilizando capacidades de cambio de tiempo y la degradación de la calidad para disminuir la potencia demandada. La solución de gestión de energía adaptativa se modela como un problema de programación lineal entera con complejidad NP-Hard. Para verificar las mejoras en la utilización de energía, se ha implementado y evaluado una solución algorítmica óptima basada en una búsqueda de fuerza bruta. Debido a la dureza del problema de gestión de energía adaptativa y el crecimiento no polinomial de su solución óptima, que se limita a la gestión de energía para un pequeño número de demandas de energía (por ejemplo, 10 demandas) y pequeños valores de los mecanismos de gestión, varias estrategias algorítmicas subóptimos más rápidos se han propuesto. En este contexto, en la primera etapa, implementamos tres estrategias heurísticas: una estrategia codiciosa (GreedyTs), una solución basada en algoritmos genéticos (GATs) y un enfoque de programación dinámica (DPTs). Luego, incorporamos tanto en la estrategia óptima como en la- heurística un método de prepartición en el que el conjunto total de servicios analizados se divide en subconjuntos de menor tamaño y complejidad que se resuelven iterativamente. Como resultado de la gestión adaptativa de la energía en esta tesis, presentamos ocho estrategias, una óptima y siete heurísticas, que cuando se despliegan en infraestructuras de comunicaciones como el dominio NFV, buscan la mejor programación posible de las demandas, que conduzcan a un uso eficiente de la energía. El desempeño de las estrategias algorítmicas ha sido validado a través de extensas simulaciones en varios escenarios, demostrando mejoras en el consumo de energía y el procesamiento de las demandas de energía. Los resultados de la simulación revelaron que los enfoques heurísticos producen soluciones de alta calidad cercanas a las óptimas mientras se ejecutan entre dos y siete órdenes de magnitud más rápido y con aplicabilidad a escenarios con miles y cientos de miles de demandas de energía. Esta tesis también explora posibles escenarios de aplicación tanto de la arquitectura propuesta para la gestión adaptativa de la energía como de las estrategias algorítmicas. En este sentido, presentamos algunos ejemplos, que incluyen sistemas de gestión de energía adaptativa en el hogar, en 5G networkPostprint (published version

QoE-Centric Control and Management of Multimedia Services in Software Defined and Virtualized Networks

Multimedia services consumption has increased tremendously since the deployment of 4G/LTE networks. Mobile video services (e.g., YouTube and Mobile TV) on smart devices are expected to continue to grow with the emergence and evolution of future networks such as 5G. The end user’s demand for services with better quality from service providers has triggered a trend towards Quality of Experience (QoE) - centric network management through efficient utilization of network resources. However, existing network technologies are either unable to adapt to diverse changing network conditions or limited in available resources. This has posed challenges to service providers for provisioning of QoE-centric multimedia services. New networking solutions such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) can provide better solutions in terms of QoE control and management of multimedia services in emerging and future networks. The features of SDN, such as adaptability, programmability and cost-effectiveness make it suitable for bandwidth-intensive multimedia applications such as live video streaming, 3D/HD video and video gaming. However, the delivery of multimedia services over SDN/NFV networks to achieve optimized QoE, and the overall QoE-centric network resource management remain an open question especially in the advent development of future softwarized networks. The work in this thesis intends to investigate, design and develop novel approaches for QoE-centric control and management of multimedia services (with a focus on video streaming services) over software defined and virtualized networks. First, a video quality management scheme based on the traffic intensity under Dynamic Adaptive Video Streaming over HTTP (DASH) using SDN is developed. The proposed scheme can mitigate virtual port queue congestion which may cause buffering or stalling events during video streaming, thus, reducing the video quality. A QoE-driven resource allocation mechanism is designed and developed for improving the end user’s QoE for video streaming services. The aim of this approach is to find the best combination of network node functions that can provide an optimized QoE level to end-users through network node cooperation. Furthermore, a novel QoE-centric management scheme is proposed and developed, which utilizes Multipath TCP (MPTCP) and Segment Routing (SR) to enhance QoE for video streaming services over SDN/NFV-based networks. The goal of this strategy is to enable service providers to route network traffic through multiple disjointed bandwidth-satisfying paths and meet specific service QoE guarantees to the end-users. Extensive experiments demonstrated that the proposed schemes in this work improve the video quality significantly compared with the state-of-the- art approaches. The thesis further proposes the path protections and link failure-free MPTCP/SR-based architecture that increases survivability, resilience, availability and robustness of future networks. The proposed path protection and dynamic link recovery scheme achieves a minimum time to recover from a failed link and avoids link congestion in softwarized networks

NFV Platforms: Taxonomy, Design Choices and Future Challenges

Due to the intrinsically inefficient service provisioning in traditional networks, Network Function Virtualization (NFV) keeps gaining attention from both industry and academia. By replacing the purpose-built, expensive, proprietary network equipment with software network functions consolidated on commodity hardware, NFV envisions a shift towards a more agile and open service provisioning paradigm. During the last few years, a large number of NFV platforms have been implemented in production environments that typically face critical challenges, including the development, deployment, and management of Virtual Network Functions (VNFs). Nonetheless, just like any complex system, such platforms commonly consist of abounding software and hardware components and usually incorporate disparate design choices based on distinct motivations or use cases. This broad collection of convoluted alternatives makes it extremely arduous for network operators to make proper choices. Although numerous efforts have been devoted to investigating different aspects of NFV, none of them specifically focused on NFV platforms or attempted to explore their design space. In this paper, we present a comprehensive survey on the NFV platform design. Our study solely targets existing NFV platform implementations. We begin with a top-down architectural view of the standard reference NFV platform and present our taxonomy of existing NFV platforms based on what features they provide in terms of a typical network function life cycle. Then we thoroughly explore the design space and elaborate on the implementation choices each platform opts for. We also envision future challenges for NFV platform design in the incoming 5G era. We believe that our study gives a detailed guideline for network operators or service providers to choose the most appropriate NFV platform based on their respective requirements. Our work also provides guidelines for implementing new NFV platforms

Genetic algorithm for holistic VNF-mapping and virtual topology design

Producción CientíficaNext generation of Internet of Things (IoT) services imposes stringent requirements to the future networks that current ones cannot fulfill. 5G is a technology born to give response to those requirements. However, the deployment of 5G is also accompanied by profound architectural changes in the network, including the introduction of technologies like multi-access edge computing (MEC), software defined networking (SDN), and network function virtualization (NFV). In particular, NFV poses diverse challenges like virtual network function (VNF) placement and chaining, also called VNF-mapping. In this paper, we present an algorithm that solves VNF-placement and chaining in a metro WDM optical network equipped with MEC resources. Therefore, it solves the VNF-mapping in conjunction with the virtual topology design of the underlying optical backhaul network. Moreover, a version of the method providing protection against node failures is also presented. A simulation study is presented to show the importance of designing the three problems jointly, in contrast to other proposals of the literature that do not take the design of the underlying network into consideration when solving that problem. Furthermore, this paper also shows the advantages of using collaboration between MEC nodes to solve the VNF-mapping problem and the advantage of using shared protection schemes. The new algorithm outperforms other proposals in terms of both service blocking ratio, and number of active CPUs (thus reducing energy consumption). Finally, the impact of deploying different physical topologies for the optical backhaul network is also presented.Ministerio de Economía, Industria y Competitividad (grant TEC2017-84423-C3-1-P)Ministerio de Industria, Comercio y Turismo (grant BES 2015-074514)Spanish Thematic Network (contract RED2018-102585-T)INTERREG V-A España-Portugal (POCTEP) program (project 0677_DISRUPTIVE_2_E