Deliverable DJRA1.2. Solutions and protocols proposal for the network control, management and monitoring in a virtualized network context

This deliverable presents several research proposals for the FEDERICA network, in different subjects, such as monitoring, routing, signalling, resource discovery, and isolation. For each topic one or more possible solutions are elaborated, explaining the background, functioning and the implications of the proposed solutions.This deliverable goes further on the research aspects within FEDERICA. First of all the architecture of the control plane for the FEDERICA infrastructure will be defined. Several possibilities could be implemented, using the basic FEDERICA infrastructure as a starting point. The focus on this document is the intra-domain aspects of the control plane and their properties. Also some inter-domain aspects are addressed. The main objective of this deliverable is to lay great stress on creating and implementing the prototype/tool for the FEDERICA slice-oriented control system using the appropriate framework. This deliverable goes deeply into the definition of the containers between entities and their syntax, preparing this tool for the future implementation of any kind of algorithm related to the control plane, for both to apply UPB policies or to configure it by hand. We opt for an open solution despite the real time limitations that we could have (for instance, opening web services connexions or applying fast recovering mechanisms). The application being developed is the central element in the control plane, and additional features must be added to this application. This control plane, from the functionality point of view, is composed by several procedures that provide a reliable application and that include some mechanisms or algorithms to be able to discover and assign resources to the user. To achieve this, several topics must be researched in order to propose new protocols for the virtual infrastructure. The topics and necessary features covered in this document include resource discovery, resource allocation, signalling, routing, isolation and monitoring. All these topics must be researched in order to find a good solution for the FEDERICA network. Some of these algorithms have started to be analyzed and will be expanded in the next deliverable. Current standardization and existing solutions have been investigated in order to find a good solution for FEDERICA. Resource discovery is an important issue within the FEDERICA network, as manual resource discovery is no option, due to scalability requirement. Furthermore, no standardization exists, so knowledge must be obtained from related work. Ideally, the proposed solutions for these topics should not only be adequate specifically for this infrastructure, but could also be applied to other virtualized networks.Postprint (published version

Characterization, design and re-optimization on multi-layer optical networks

L'augment de volum de tràfic IP provocat per l'increment de serveis multimèdia com HDTV o vídeo conferència planteja nous reptes als operadors de xarxa per tal de proveir transmissió de dades eficient. Tot i que les xarxes mallades amb multiplexació per divisió de longitud d'ona (DWDM) suporten connexions òptiques de gran velocitat, aquestes xarxes manquen de flexibilitat per suportar tràfic d’inferior granularitat, fet que provoca un pobre ús d'ample de banda. Per fer front al transport d'aquest tràfic heterogeni, les xarxes multicapa representen la millor solució. Les xarxes òptiques multicapa permeten optimitzar la capacitat mitjançant l'empaquetament de connexions de baixa velocitat dins de connexions òptiques de gran velocitat. Durant aquesta operació, es crea i modifica constantment una topologia virtual dinàmica gràcies al pla de control responsable d’aquestes operacions. Donada aquesta dinamicitat, un ús sub-òptim de recursos pot existir a la xarxa en un moment donat. En aquest context, una re-optimizació periòdica dels recursos utilitzats pot ser aplicada, millorant així l'ús de recursos. Aquesta tesi està dedicada a la caracterització, planificació, i re-optimització de xarxes òptiques multicapa de nova generació des d’un punt de vista unificat incloent optimització als nivells de capa física, capa òptica, capa virtual i pla de control. Concretament s'han desenvolupat models estadístics i de programació matemàtica i meta-heurístiques. Aquest objectiu principal s'ha assolit mitjançant cinc objectius concrets cobrint diversos temes oberts de recerca. En primer lloc, proposem una metodologia estadística per millorar el càlcul del factor Q en problemes d'assignació de ruta i longitud d'ona considerant interaccions físiques (IA-RWA). Amb aquest objectiu, proposem dos models estadístics per computar l'efecte XPM (el coll d'ampolla en termes de computació i complexitat) per problemes IA-RWA, demostrant la precisió d’ambdós models en el càlcul del factor Q en escenaris reals de tràfic. En segon lloc i fixant-nos a la capa òptica, presentem un nou particionament del conjunt de longituds d'ona que permet maximitzar, respecte el cas habitual, la quantitat de tràfic extra proveït en entorns de protecció compartida. Concretament, definim diversos models estadístics per estimar la quantitat de tràfic donat un grau de servei objectiu, i diferents models de planificació de xarxa amb l'objectiu de maximitzar els ingressos previstos i el valor actual net de la xarxa. Després de resoldre aquests problemes per xarxes reals, concloem que la nostra proposta maximitza ambdós objectius. En tercer lloc, afrontem el disseny de xarxes multicapa robustes davant de fallida simple a la capa IP/MPLS i als enllaços de fibra. Per resoldre aquest problema eficientment, proposem un enfocament basat en sobre-dimensionar l'equipament de la capa IP/MPLS i recuperar la connectivitat i el comparem amb la solució convencional basada en duplicar la capa IP/MPLS. Després de comparar solucions mitjançant models ILP i heurístiques, concloem que la nostra solució permet obtenir un estalvi significatiu en termes de costos de desplegament. Com a quart objectiu, introduïm un mecanisme adaptatiu per reduir l'ús de ports opto-electrònics (O/E) en xarxes multicapa sota escenaris de tràfic dinàmic. Una formulació ILP i diverses heurístiques són desenvolupades per resoldre aquest problema, que permet reduir significativament l’ús de ports O/E en temps molt curts. Finalment, adrecem el problema de disseny resilient del pla de control GMPLS. Després de proposar un nou model analític per quantificar la resiliència en topologies mallades de pla de control, usem aquest model per proposar un problema de disseny de pla de control. Proposem un procediment iteratiu lineal i una heurística i els usem per resoldre instàncies reals, arribant a la conclusió que es pot reduir significativament la quantitat d'enllaços del pla de control sense afectar la qualitat de servei a la xarxa.The explosion of IP traffic due to the increase of IP-based multimedia services such as HDTV or video conferencing poses new challenges to network operators to provide a cost-effective data transmission. Although Dense Wavelength Division Multiplexing (DWDM) meshed transport networks support high-speed optical connections, these networks lack the flexibility to support sub-wavelength traffic leading to poor bandwidth usage. To cope with the transport of that huge and heterogeneous amount of traffic, multilayer networks represent the most accepted architectural solution. Multilayer optical networks allow optimizing network capacity by means of packing several low-speed traffic streams into higher-speed optical connections (lightpaths). During this operation, a dynamic virtual topology is created and modified the whole time thanks to a control plane responsible for the establishment, maintenance, and release of connections. Because of this dynamicity, a suboptimal allocation of resources may exist at any time. In this context, a periodically resource reallocation could be deployed in the network, thus improving network resource utilization. This thesis is devoted to the characterization, planning, and re-optimization of next-generation multilayer networks from an integral perspective including physical layer, optical layer, virtual layer, and control plane optimization. To this aim, statistical models, mathematical programming models and meta-heuristics are developed. More specifically, this main objective has been attained by developing five goals covering different open issues. First, we provide a statistical methodology to improve the computation of the Q-factor for impairment-aware routing and wavelength assignment problems (IA-RWA). To this aim we propose two statistical models to compute the Cross-Phase Modulation variance (which represents the bottleneck in terms of computation time and complexity) in off-line and on-line IA-RWA problems, proving the accuracy of both models when computing Q-factor values in real traffic scenarios. Second and moving to the optical layer, we present a new wavelength partitioning scheme that allows maximizing the amount of extra traffic provided in shared path protected environments compared with current solutions. Specifically, we define several statistical models to estimate the traffic intensity given a target grade of service, and different network planning problems for maximizing the expected revenues and net present value. After solving these problems for real networks, we conclude that our proposed scheme maximizes both revenues and NPV. Third, we tackle the design of survivable multilayer networks against single failures at the IP/MPLS layer and WSON links. To efficiently solve this problem, we propose a new approach based on over-dimensioning IP/MPLS devices and lightpath connectivity and recovery and we compare it against the conventional solution based on duplicating backbone IP/MPLS nodes. After evaluating both approaches by means of ILP models and heuristic algorithms, we conclude that our proposed approach leads to significant CAPEX savings. Fourth, we introduce an adaptive mechanism to reduce the usage of opto-electronic (O/E) ports of IP/MPLS-over-WSON multilayer networks in dynamic scenarios. A ILP formulation and several heuristics are developed to solve this problem, which allows significantly reducing the usage of O/E ports in very short running times. Finally, we address the design of resilient control plane topologies in GMPLS-enabled transport networks. After proposing a novel analytical model to quantify the resilience in mesh control plane topologies, we use this model to propose a problem to design the control plane topology. An iterative model and a heuristic are proposed and used to solve real instances, concluding that a significant reduction in the number of control plane links can be performed without affecting the quality of service of the network

Learning Augmented Optimization for Network Softwarization in 5G

The rapid uptake of mobile devices and applications are posing unprecedented traffic burdens on the existing networking infrastructures. In order to maximize both user experience and investment return, the networking and communications systems are evolving to the next gen- eration – 5G, which is expected to support more flexibility, agility, and intelligence towards provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and expanded with large sizes. Network softwarization is one of the critical enabling technologies to implement these requirements in 5G. In addition to these problems investigated in preliminary researches about this technology, many new emerging application requirements and advanced opti- mization & learning technologies are introducing more challenges & opportunities for its fully application in practical production environment. This motivates this thesis to develop a new learning augmented optimization technology, which merges both the advanced opti- mization and learning techniques to meet the distinct characteristics of the new application environment. To be more specific, the abstracts of the key contents in this thesis are listed as follows: • We first develop a stochastic solution to augment the optimization of the Network Function Virtualization (NFV) services in dynamical networks. In contrast to the dominant NFV solutions applied for the deterministic networking environments, the inherent network dynamics and uncertainties from 5G infrastructure are impeding the rollout of NFV in many emerging networking applications. Therefore, Chapter 3 investigates the issues of network utility degradation when implementing NFV in dynamical networks, and proposes a robust NFV solution with full respect to the underlying stochastic features. By exploiting the hierarchical decision structures in this problem, a distributed computing framework with two-level decomposition is designed to facilitate a distributed implementation of the proposed model in large-scale networks. • Next, Chapter 4 aims to intertwin the traditional optimization and learning technologies. In order to reap the merits of both optimization and learning technologies but avoid their limitations, promissing integrative approaches are investigated to combine the traditional optimization theories with advanced learning methods. Subsequently, an online optimization process is designed to learn the system dynamics for the network slicing problem, another critical challenge for network softwarization. Specifically, we first present a two-stage slicing optimization model with time-averaged constraints and objective to safeguard the network slicing operations in time-varying networks. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. To address this, we combine the historical learning and Lyapunov stability theories, and develop a learning augmented online optimization approach. This facilitates the system to learn a safe slicing solution from both historical records and real-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, simulation experiments are also provided to demonstrate the considerable improvement of the proposals. • The success of traditional solutions to optimizing the stochastic systems often requires solving a base optimization program repeatedly until convergence. For each iteration, the base program exhibits the same model structure, but only differing in their input data. Such properties of the stochastic optimization systems encourage the work of Chapter 5, in which we apply the latest deep learning technologies to abstract the core structures of an optimization model and then use the learned deep learning model to directly generate the solutions to the equivalent optimization model. In this respect, an encoder-decoder based learning model is developed in Chapter 5 to improve the optimization of network slices. In order to facilitate the solving of the constrained combinatorial optimization program in a deep learning manner, we design a problem-specific decoding process by integrating program constraints and problem context information into the training process. The deep learning model, once trained, can be used to directly generate the solution to any specific problem instance. This avoids the extensive computation in traditional approaches, which re-solve the whole combinatorial optimization problem for every instance from the scratch. With the help of the REINFORCE gradient estimator, the obtained deep learning model in the experiments achieves significantly reduced computation time and optimality loss

Using GRASP and GA to design resilient and cost-effective IP/MPLS networks

The main objective of this thesis is to find good quality solutions for representative instances of the problem of designing a resilient and low cost IP/MPLS network, to be deployed over an existing optical transport network. This research is motivated by two complementary real-world application cases, which comprise the most important commercial and academic networks of Uruguay. To achieve this goal, we performed an exhaustive analysis of existing models and technologies. From all of them we took elements that were contrasted with the particular requirements of our counterparts. We highlight among these requirements, the need of getting solutions transparently implementable over a heterogeneous network environment, which limit us to use widely standardized features of related technologies. We decided to create new models more suitable to fit these needs. These models are intrinsically hard to solve (NP-Hard). Thus we developed metaheuristic based algorithms to find solutions to these real-world instances. Evolutionary Algorithms and Greedy Randomized Adaptive Search Procedures obtained the best results. As it usually happens, real-world planning problems are surrounded by uncertainty. Therefore, we have worked closely with our counterparts to reduce the fuzziness upon data to a set of representative cases. They were combined with different strategies of design to get to scenarios, which were translated into instances of these problems. Finally, the algorithms were fed with this information, and from their outcome we derived our results and conclusions

Optimal route reflection topology design

An Autonomous System (AS) is a group of Internet Protocol-based networks with a single and clearly defined external routing policy, usually under single ownership, trust or administrative control. The AS represents a connected group of one or more blocks of IP addresses, called IP prefixes, that have been assigned to that organization and provides a single routing policy to systems outside the AS. The Internet is composed of the interconnection of several thousands of ASes, which use the Border Gateway Protocol (BGP) to exchange network prefixes (aggregations of IP addresses) reachability advertisements. BGP advertisements (or updates) are sent over BGP sessions administratively set between pairs of routers. BGP is a path vector routing protocol and is used to span different ASes. A path vector protocol defines a route as a pairing between a destination and the attributes of the path to that destination. Interior Border Gateway Protocol (iBGP) refers to the BGP neighbor relationship within the same AS. When BGP neighbor relationship are formed between two peers belonging to different AS are called Exterior Border Gateway Protocol (eBGP). In the last case, BGP routers are called Autonomous System Border Routers (ASBRs), while those running only iBGP sessions are referred to as Internal Routers (IRs). Traditional iBGP implementations require a full-mesh of sessions among routers of each AS

Traffic engineering in dynamic optical networks

Traffic Engineering (TE) refers to all the techniques a Service Provider employs to improve the efficiency and reliability of network operations. In IP over Optical (IPO) networks, traffic coming from upper layers is carried over the logical topology defined by the set of established lightpaths. Within this framework then, TE techniques allow to optimize the configuration of optical resources with respect to an highly dynamic traffic demand. TE can be performed with two main methods: if the demand is known only in terms of an aggregated traffic matrix, the problem of automatically updating the configuration of an optical network to accommodate traffic changes is called Virtual Topology Reconfiguration (VTR). If instead the traffic demand is known in terms of data-level connection requests with sub-wavelength granularity, arriving dynamically from some source node to any destination node, the problem is called Dynamic Traffic Grooming (DTG). In this dissertation new VTR algorithms for load balancing in optical networks based on Local Search (LS) techniques are presented. The main advantage of using LS is the minimization of network disruption, since the reconfiguration involves only a small part of the network. A comparison between the proposed schemes and the optimal solutions found via an ILP solver shows calculation time savings for comparable results of network congestion. A similar load balancing technique has been applied to alleviate congestion in an MPLS network, based on the efficient rerouting of Label-Switched Paths (LSP) from the most congested links to allow a better usage of network resources. Many algorithms have been developed to deal with DTG in IPO networks, where most of the attention is focused on optimizing the physical resources utilization by considering specific constraints on the optical node architecture, while very few attention has been put so far on the Quality of Service (QoS) guarantees for the carried traffic. In this thesis a novel Traffic Engineering scheme is proposed to guarantee QoS from both the viewpoint of service differentiation and transmission quality. Another contribution in this thesis is a formal framework for the definition of dynamic grooming policies in IPO networks. The framework is then specialized for an overlay architecture, where the control plane of the IP and optical level are separated, and no information is shared between the two. A family of grooming policies based on constraints on the number of hops and on the bandwidth sharing degree at the IP level is defined, and its performance analyzed in both regular and irregular topologies. While most of the literature on DTG problem implicitly considers the grooming of low-speed connections onto optical channels using a TDM approach, the proposed grooming policies are evaluated here by considering a realistic traffic model which consider a Dynamic Statistical Multiplexing (DSM) approach, i.e. a single wavelength channel is shared between multiple IP elastic traffic flows

Cooperative Radio Communications for Green Smart Environments

The demand for mobile connectivity is continuously increasing, and by 2020 Mobile and Wireless Communications will serve not only very dense populations of mobile phones and nomadic computers, but also the expected multiplicity of devices and sensors located in machines, vehicles, health systems and city infrastructures. Future Mobile Networks are then faced with many new scenarios and use cases, which will load the networks with different data traffic patterns, in new or shared spectrum bands, creating new specific requirements. This book addresses both the techniques to model, analyse and optimise the radio links and transmission systems in such scenarios, together with the most advanced radio access, resource management and mobile networking technologies. This text summarises the work performed by more than 500 researchers from more than 120 institutions in Europe, America and Asia, from both academia and industries, within the framework of the COST IC1004 Action on "Cooperative Radio Communications for Green and Smart Environments". The book will have appeal to graduates and researchers in the Radio Communications area, and also to engineers working in the Wireless industry. Topics discussed in this book include: • Radio waves propagation phenomena in diverse urban, indoor, vehicular and body environments• Measurements, characterization, and modelling of radio channels beyond 4G networks• Key issues in Vehicle (V2X) communication• Wireless Body Area Networks, including specific Radio Channel Models for WBANs• Energy efficiency and resource management enhancements in Radio Access Networks• Definitions and models for the virtualised and cloud RAN architectures• Advances on feasible indoor localization and tracking techniques• Recent findings and innovations in antenna systems for communications• Physical Layer Network Coding for next generation wireless systems• Methods and techniques for MIMO Over the Air (OTA) testin

Aspects of proactive traffic engineering in IP networks

To deliver a reliable communication service over the Internet it is essential for the network operator to manage the traffic situation in the network. The traffic situation is controlled by the routing function which determines what path traffic follows from source to destination. Current practices for setting routing parameters in IP networks are designed to be simple to manage. This can lead to congestion in parts of the network while other parts of the network are far from fully utilized. In this thesis we explore issues related to optimization of the routing function to balance load in the network and efficiently deliver a reliable communication service to the users. The optimization takes into account not only the traffic situation under normal operational conditions, but also traffic situations that appear under a wide variety of circumstances deviating from the nominal case. In order to balance load in the network knowledge of the traffic situations is needed. Consequently, in this thesis we investigate methods for efficient derivation of the traffic situation. The derivation is based on estimation of traffic demands from link load measurements. The advantage of using link load measurements is that they are easily obtained and consist of a limited amount of data that need to be processed. We evaluate and demonstrate how estimation based on link counts gives the operator a fast and accurate description of the traffic demands. For the evaluation we have access to a unique data set of complete traffic demands from an operational IP backbone. However, to honor service level agreements at all times the variability of the traffic needs to be accounted for in the load balancing. In addition, optimization techniques are often sensitive to errors and variations in input data. Hence, when an optimized routing setting is subjected to real traffic demands in the network, performance often deviate from what can be anticipated from the optimization. Thus, we identify and model different traffic uncertainties and describe how the routing setting can be optimized, not only for a nominal case, but for a wide range of different traffic situations that might appear in the network. Our results can be applied in MPLS enabled networks as well as in networks using link state routing protocols such as the widely used OSPF and IS-IS protocols. Only minor changes may be needed in current networks to implement our algorithms. The contributions of this thesis is that we: demonstrate that it is possible to estimate the traffic matrix with acceptable precision, and we develop methods and models for common traffic uncertainties to account for these uncertainties in the optimization of the routing configuration. In addition, we identify important properties in the structure of the traffic to successfully balance uncertain and varying traffic demands