Eva Marín-Tordera

A hierarchical routing approach for GMPLS based control plane for ASON

A hierarchical network architecture is one of the hard recommendations stated at the automatically switched optical networks (ASON) specifications. This paper focuses on providing ASON with a hierarchical routing in order to ensure the scalability for large worldwide networks, mainly focusing on the functionality expected from GMPLS routing, such as aggregation schemes and routing algorithms, targeting to optimize the global network performance while guaranteeing scalability. Simulation results indeed show the benefits obtained owing to harder overhead reduction without impacting on the blocking probability.

Hierarchical Routing with QoS Constraints in Optical Transport Networks

Optical Transport Networks (OTN) with automatical switching capabilities are named ASON. Hierarchical routing is required in the ASON recommendations to achieve scalability. Basically, hierarchical routing consists of three main components, an aggregation scheme, an update policy and a routing algorithm. This paper proposes a new network structure focusing on these three components. We propose a new aggregation scheme; an update policy based on a threshold value; and we also extend an already proposed routing mechanism to be applied to a hierarchical network. Main skill of this routing mechanism is to reduce the connection blocking increase because of selecting paths based on inaccurate routing information. This inaccuracy is introduced both by the aggregation process and by the update policy.

Handling service allocation in combined Fog-cloud scenarios

The recent technological advances related to computing, storage, cloud, networking and the unstoppable deployment of end-user devices, are all coining the so-called Internet of Things (IoT). IoT embraces a wide set of heterogeneous services in highly impacting societal sectors, such as Healthcare, Smart Transportation or Media delivery, all of them posing a diverse set of requirements, including real time response, low latency, or high capacity. In order to properly address such diverse set of requirements, the combined use of Cloud and Fog computing turns up as an emerging trend. Indeed, Fog provides low delay for services demanding real time response, constrained to support low capacity queries, whereas Cloud provides high capacity at the cost of a higher latency. It is with no doubt that a new strategy is required to ease the combined operation of cloud and fog infrastructures in IoT scenarios, also referred to as Combined Fog-Cloud (CFC), in terms of service execution performance metrics. To that end, in this paper, we introduce and formulate the QoS-aware service allocation problem for CFC architectures as an integer optimization problem, whose solution minimizes the latency experienced by the services while guaranteeing the fulfillment of the capacity requirements.

A hierarchical routing approach for optical transport networks

Although the automatically switched optical networks (ASON) specifications strongly recommend a hierarchical network architecture for these networks, this is still an open issue. The hierarchical network concept involves several mechanisms mainly related with signaling and routing, such as the aggregation scheme, the dissemination process, the updating policy and the routing algorithms. The existing mechanisms for flat networks must be substantially modified to be applied to a hierarchical network architecture. In this paper, authors propose a complete hierarchical routing approach mainly focusing on routing concerns, aiming to optimize the global network performance while guaranteeing scalability.

Reducing the effects of routing inaccuracy by means of prediction and an innovative link-state cost

The routing inaccuracy problem is one of the major issues impeding the evolution and deployment of Constraint-Based Routing (CBR) techniques. This paper proposes a promising CBR strategy that combines the strengths of prediction with an innovative link-state cost. The latter explicitly integrates a two-bit counter predictor, with a novel metric that stands for the degree of inaccuracy (seen by the source node) of the state information associated with the links along a path. In our routing model, Link-State Advertisements (LSAs) are only distributed upon topological changes in the network, i.e., the state and availability of network resources along a path are predicted from the source rather than updated through conventional LSAs. As a proof-of-concept, we apply our routing strategy in the context of circuit-switched networks. We show that our approach considerably reduces the impact of routing inaccuracy on the blocking probability, while eliminating the typical LSAs caused by the traffic dynamics in CBR protocols.

The prediction-based routing in optical transport networks

In optical networks, the traditional routing problem is generally decoupled into two subproblems, the route selection and the wavelength assignment. Usual RWA (Routing and Wavelength Assignment) algorithms take the lightpath decision based on the network state information existing in those nodes caring about it. Unfortunately, this information might not be accurate enough so that the routing decisions could be incorrectly performed hence driving to a significant connection blocking increment. The mechanism proposed in this paper aims to optimize the network performance while reducing the signalling overhead required to keep updated network state information on all the network nodes. The novel idea introduced in the Prediction Based Routing allows lightpaths to be computed not according to the potentially inaccurate network state information but according to a prediction scheme. In this way, flooding update messages is not needed so that the signalling overhead is significantly reduced. After analyzing the PBR behaviour, we conclude that the PBR performs better than usual RWA algorithms (such as the Shortest-Path/Least-Loaded) in different scenarios of traffic loads and available resources.

Do we all really know what a fog node is? Current trends towards an open definition

Fog computing has emerged as a promising technology that can bring cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and particularly a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a “mini-cloud” located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and end up showing how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.

OBGP+: An improved path-vector protocol for multi-domain optical networks

One of the essential components for the dynamic provisioning of lightpaths across multiple domains is the Routing and Wavelength Assignment (RWA) strategy adopted. The consolidation that path-vector protocols have had in practice, has motivated the optical extension to BGP (OBGP). We claim, however, that a routing model mostly centered on the exchange of reachability information-like the one we have today with BGP or the one offered by OBGP-will not be sufficient for multi-domain optical networks. Routing domains must be able to exchange both reachability as well as aggregated Path-State Information (PSI). Understanding that this is a missing piece in the routing models provided by BGP and OBGP is easy nowadays, but contributing with solutions capable of highly improving the performance of a path-vector without impacting on key aspects of the protocol-fundamentally, its scalability, its convergence properties, and the number of routing messages exchanged between domains-is a challenging task. In this paper we propose OBGP +, which is a very simple extension of a path-vector protocol supporting the computation and advertisement of PSI between optical domains. The PSI that we propose to use is highly condensed in the form of a single integer value. In order to avoid the typical increase in the number of routing messages associated with the update of PSI, we propose to piggy-back the updates in non-dummy Keepalive messages exchanged between OBGP+ neighbors. Extensive simulations reveal that, despite its simplicity: (i) OBGP+ is able to drastically reduce the blocking experienced with a path-vector protocol like OBGP; (ii) OBGP+ needs much less number of routing messages than OBGP to achieve such performance; and (iii) the convergence and restoration features of OBGP+ are also better than those of OBGP, which is particularly important for connections that lack a protection path.

Towards Distributed Service Allocation in Fog-to-Cloud (F2C) Scenarios

The novel Fog-to-Cloud (F2C) computing paradigm has been recently proposed aiming at the enhanced integration of Fog Computing and Cloud Computing through the coordinated management of underlying resources, taking into account the peculiarities inherent to each computing model, and enabling the parallel and distributed execution of services into distinct fog/cloud resources. Nevertheless, studies on F2C are still premature and several issues remain unsolved yet. For instance, in an F2C scenario service allocation must cope with the specific aspects associated to cloud and fog resource models, requiring distinct strategies to properly map IoT services into the most suitable available resources. In this paper, we propose a QoS-aware service distribution strategy contemplating both service requirements and resource offerings. We model the service allocation problem as a multidimensional knapsack problem (MKP) aiming at an optimal service allocation taking into consideration delay, load balancing and energy consumption. The presented results, demonstrate that the adopted strategy may be applied by F2C computing reducing the service allocation delay, while also diminishing load and energy consumption on cloud and fog resources.

Estimating Smart City sensors data generation

Nowadays, Smart Cities are positioned as one of the most challenging and important research topics, highlighting major changes in peoples lifestyle. Technologies such as smart energy, smart transportation or smart health are being designed to improve citizens quality of life. Smart Cities leverage the deployment of a network of devices - sensors and mobile devices-, all connected through different means and/or technologies, according to their network availability and capacities, setting a novel framework feeding end-users with an innovative set of smart services. Aligned to this objective, a typical Smart City architecture is organized into layers, including a sensing layer (generates data), a network layer (moves the data), a middleware layer (manages all collected data and makes it ready for usage) and an application layer (provides the smart services benefiting from this data). In this paper a real Smart City is analyzed, corresponding to the city of Barcelona, with special emphasis on the layers responsible for collecting the data generated by the deployed sensors. The amount of daily sensors data transmitted through the network has been estimated and a rough projection has been made assuming an exhaustive deployment that fully covers all city. Finally, we discuss some solutions to both reduce the data transmission and improve the data management.