SDN/NFV-enabled satellite communications networks: opportunities, scenarios and challenges

In the context of next generation 5G networks, the satellite industry is clearly committed to revisit and revamp the role of satellite communications. As major drivers in the evolution of (terrestrial) fixed and mobile networks, Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies are also being positioned as central technology enablers towards improved and more flexible integration of satellite and terrestrial segments, providing satellite network further service innovation and business agility by advanced network resources management techniques. Through the analysis of scenarios and use cases, this paper provides a description of the benefits that SDN/NFV technologies can bring into satellite communications towards 5G. Three scenarios are presented and analysed to delineate different potential improvement areas pursued through the introduction of SDN/NFV technologies in the satellite ground segment domain. Within each scenario, a number of use cases are developed to gain further insight into specific capabilities and to identify the technical challenges stemming from them.Peer ReviewedPostprint (author's final draft

Resource Allocation in SDN/NFV-Enabled Core Networks

For next generation core networks, it is anticipated to integrate communication, storage and computing resources into one unified, programmable and flexible infrastructure. Software-defined networking (SDN) and network function virtualization (NFV) become two enablers. SDN decouples the network control and forwarding functions, which facilitates network management and enables network programmability. NFV allows the network functions to be virtualized and placed on high capacity servers located anywhere in the network, not only on dedicated devices in current networks. Driven by SDN and NFV platforms, the future network architecture is expected to feature centralized network management, virtualized function chaining, reduced capital and operational costs, and enhanced service quality. The combination of SDN and NFV provides a potential technical route to promote the future communication networks. It is imperative to efficiently manage, allocate and optimize the heterogeneous resources, including computing, storage, and communication resources, to the customized services to achieve better quality-of-service (QoS) provisioning. This thesis makes some in-depth researches on efficient resource allocation for SDN/NFV-enabled core networks in multiple aspects and dimensionality. Typically, the resource allocation task is implemented in three aspects. Given the traffic metrics, QoS requirements, and resource constraints of the substrate network, we first need to compose a virtual network function (VNF) chain to form a virtual network (VN) topology. Then, virtual resources allocated to each VNF or virtual link need to be optimized in order to minimize the provisioning cost while satisfying the QoS requirements. Next, we need to embed the virtual network (i.e., VNF chain) onto the substrate network, in which we need to assign the physical resources in an economical way to meet the resource demands of VNFs and links. This involves determining the locations of NFV nodes to host the VNFs and the routing from source to destination. Finally, we need to schedule the VNFs for multiple services to minimize the service completion time and maximize the network performance. In this thesis, we study resource allocation in SDN/NFV-enabled core networks from the aforementioned three aspects. First, we jointly study how to design the topology of a VN and embed the resultant VN onto a substrate network with the objective of minimizing the embedding cost while satisfying the QoS requirements. In VN topology design, optimizing the resource requirement for each virtual node and link is necessary. Without topology optimization, the resources assigned to the virtual network may be insufficient or redundant, leading to degraded service quality or increased embedding cost. The joint problem is formulated as a Mixed Integer Nonlinear Programming (MINLP), where queueing theory is utilized as the methodology to analyze the network delay and help to define the optimal set of physical resource requirements at network elements. Two algorithms are proposed to obtain the optimal/near-optimal solutions of the MINLP model. Second, we address the multi-SFC embedding problem by a game theoretical approach, considering the heterogeneity of NFV nodes, the effect of processing-resource sharing among various VNFs, and the capacity constraints of NFV nodes. In the proposed resource constrained multi-SFC embedding game (RC-MSEG), each SFC is treated as a player whose objective is to minimize the overall latency experienced by the supported service flow, while satisfying the capacity constraints of all its NFV nodes. Due to processing-resource sharing, additional delay is incurred and integrated into the overall latency for each SFC. The capacity constraints of NFV nodes are considered by adding a penalty term into the cost function of each player, and are guaranteed by a prioritized admission control mechanism. We first prove that the proposed game RC-MSEG is an exact potential game admitting at least one pure Nash Equilibrium (NE) and has the finite improvement property (FIP). Then, we design two iterative algorithms, namely, the best response (BR) algorithm with fast convergence and the spatial adaptive play (SAP) algorithm with great potential to obtain the best NE of the proposed game. Third, the VNF scheduling problem is investigated to minimize the makespan (i.e., overall completion time) of all services, while satisfying their different end-to-end (E2E) delay requirements. The problem is formulated as a mixed integer linear program (MILP) which is NP-hard with exponentially increasing computational complexity as the network size expands. To solve the MILP with high efficiency and accuracy, the original problem is reformulated as a Markov decision process (MDP) problem with variable action set. Then, a reinforcement learning (RL) algorithm is developed to learn the best scheduling policy by continuously interacting with the network environment. The proposed learning algorithm determines the variable action set at each decision-making state and accommodates different execution time of the actions. The reward function in the proposed algorithm is carefully designed to realize delay-aware VNF scheduling. To sum up, it is of great importance to integrate SDN and NFV in the same network to accelerate the evolution toward software-enabled network services. We have studied VN topology design, multi-VNF chain embedding, and delay-aware VNF scheduling to achieve efficient resource allocation in different dimensions. The proposed approaches pave the way for exploiting network slicing to improve resource utilization and facilitate QoS-guaranteed service provisioning in SDN/NFV-enabled networks

Progressive introduction of network softwarization in operational telecom networks: advances at architectural, service and transport levels

Technological paradigms such as Software Defined Networking, Network Function Virtualization and Network Slicing are altogether offering new ways of providing services. This process is widely known as Network Softwarization, where traditional operational networks adopt capabilities and mechanisms inherit form the computing world, such as programmability, virtualization and multi-tenancy. This adoption brings a number of challenges, both from the technological and operational perspectives. On the other hand, they provide an unprecedented flexibility opening opportunities to developing new services and new ways of exploiting and consuming telecom networks. This Thesis first overviews the implications of the progressive introduction of network softwarization in operational networks for later on detail some advances at different levels, namely architectural, service and transport levels. It is done through specific exemplary use cases and evolution scenarios, with the goal of illustrating both new possibilities and existing gaps for the ongoing transition towards an advanced future mode of operation. This is performed from the perspective of a telecom operator, paying special attention on how to integrate all these paradigms into operational networks for assisting on their evolution targeting new, more sophisticated service demands.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Eduardo Juan Jacob Taquet.- Secretario: Francisco Valera Pintor.- Vocal: Jorge López Vizcaín

A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing

On the Orchestration and Provisioning of NFV-enabled Multicast Services

The paradigm of network function virtualization (NFV) with the support of software-defined networking has emerged as a prominent approach to foster innovation in the networking field and reduce the complexity involved in managing modern-day conventional networks. Before NFV, functions, which can manipulate the packet header and context of traffic flow, used to be implemented at fixed locations in the network substrate inside proprietary physical devices (called middlewares). With NFV, such functions are softwarized and virtualized. As such, they can be deployed in commodity servers as demanded. Hence, the provisioning of a network service becomes more agile and abstract, thereby giving rise to the next-generation service-customized networks which have the potential to meet new demands and use cases. In this thesis, we focus on three complementary research problems essential to the orchestration and provisioning of NFV-enabled multicast network services. An NFV-enabled multicast service connects a source with a set of destinations. It specifies a set of NFs that should be executed at the chosen routes from the source to the destinations, with some resources and ordering relationships that should be satisfied in wired core networks. In Problem I, we investigate a static joint traffic routing and virtual NF placement framework for accommodating multicast services over the network substrate. We develop optimal formulations and efficient heuristic algorithms that jointly handle the static embedding of one or multiple service requests over the network substrate with single-path and multipath routing. In Problem II, we study the online orchestration of NFV-enabled network services. We consider both unicast and multicast NFV-enabled services with mandatory and best-effort NF types. Mandatory NFs are strictly necessary for the correctness of a network service, whereas best-effort NFs are preferable yet not necessary. Correspondingly, we propose a primal-dual based online approximation algorithm that allocates both processing and transmission resources to maximize a profit function that is proportional to the throughput. The online algorithm resembles a joint admission mechanism and an online composition, routing, and NF placement framework. In the core network, traffic patterns exhibit time-varying characteristics that can be cumbersome to model. Therefore, in Problem III, we develop a dynamic provisioning approach to allocate processing and transmission resources based on the traffic pattern of the embedded network service using deep reinforcement learning (RL). Notably, we devise a model-assisted exploration procedure to improve the efficiency and consistency of the deep RL algorithm

On-demand network slicing using SDN/NFV-enabled satellite ground segment systems

© 2018 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 works.This paper proposes an architecture framework for the realization of on-demand satellite network slicing that is built on the introduction of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. In this way, service delivery with satellite networks is shifted from a network for connectivity model to a network for service model with a high degree of service customization and adaptability, including satellite bandwidth on-demand. Under this framework, we study the resource orchestration of satellite network services by formulating the on-demand network slicing as an optimization problem that provides flexible service chaining and provisioning taking into account diversified service requirements. The objective is to determine the optimal resource allocation for supporting a satellite network slice that minimizes resources consumption while meeting service specification requirements such as the end-to-end delay.Peer ReviewedPostprint (author's final draft

Enabling multicast slices in edge networks

Telecommunication networks are undergoing a disruptive transition towards distributed mobile edge networks with virtualized network functions (VNFs) (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) within the proximity of users. This transition will enable network services, especially IoT applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this paper we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into Integer Linear Programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a non-trivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on Reinforcement Learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real test-bed. Experimental results demonstrate that the proposed algorithms is promising