Resilient network dimensioning for optical grid/clouds using relocation

In this paper we address the problem of dimensioning infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We will provide an overview of our work in this area, and in particular focus on how to design the resulting grid/cloud to be resilient against network link and/or server site failures. To this end, we will exploit relocation: under failure conditions, a request may be sent to an alternate destination than the one under failure-free conditions. We will provide a comprehensive overview of related work in this area, and focus in some detail on our own most recent work. The latter comprises a case study where traffic has a known origin, but we assume a degree of freedom as to where its end up being processed, which is typically the case for e. g., grid applications of the bag-of-tasks (BoT) type or for providing cloud services. In particular, we will provide in this paper a new integer linear programming (ILP) formulation to solve the resilient grid/cloud dimensioning problem using failure-dependent backup routes. Our algorithm will simultaneously decide on server and network capacity. We find that in the anycast routing problem we address, the benefit of using failure-dependent (FD) rerouting is limited compared to failure-independent (FID) backup routing. We confirm our earlier findings in terms of network capacity savings achieved by relocation compared to not exploiting relocation (order of 6-10% in the current case studies)

Joint dimensioning of server and network infrastructure for resilient optical grids/clouds

We address the dimensioning of infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We design the resulting grid/cloud to be resilient against network link or server failures. To this end, we exploit relocation: Under failure conditions, a grid job or cloud virtual machine may be served at an alternate destination (i.e., different from the one under failure-free conditions). We thus consider grid/cloud requests to have a known origin, but assume a degree of freedom as to where they end up being served, which is the case for grid applications of the bag-of-tasks (BoT) type or hosted virtual machines in the cloud case. We present a generic methodology based on integer linear programming (ILP) that: 1) chooses a given number of sites in a given network topology where to install server infrastructure; and 2) determines the amount of both network and server capacity to cater for both the failure-free scenario and failures of links or nodes. For the latter, we consider either failure-independent (FID) or failure-dependent (FD) recovery. Case studies on European-scale networks show that relocation allows considerable reduction of the total amount of network and server resources, especially in sparse topologies and for higher numbers of server sites. Adopting a failure-dependent backup routing strategy does lead to lower resource dimensions, but only when we adopt relocation (especially for a high number of server sites): Without exploiting relocation, potential savings of FD versus FID are not meaningful

Scalable algorithms for QoS-aware virtual network mapping for cloud services

Both business and consumer applications increasingly depend on cloud solutions. Yet, many are still reluctant to move to cloud-based solutions, mainly due to concerns of service quality and reliability. Since cloud platforms depend both on IT resources (located in data centers, DCs) and network infrastructure connecting to it, both QoS and resilience should be offered with end-to-end guarantees up to and including the server resources. The latter currently is largely impeded by the fact that the network and cloud DC domains are typically operated by disjoint entities. Network virtualization, together with combined control of network and IT resources can solve that problem. Here, we formally state the combined network and IT provisioning problem for a set of virtual networks, incorporating resilience as well as QoS in physical and virtual layers. We provide a scalable column generation model, to address real world network sizes. We analyze the latter in extensive case studies, to answer the question at which layer to provision QoS and resilience in virtual networks for cloud services

Real-Time Energy Price-Aware Anycast RWA for Scheduled Lightpath Demands in Optical Data Center Networks

The energy consumption of the data center networks and the power consumption associated with transporting data to the users is considerably large, and it constitutes a significant portion of their costs. Hence, development of energy efficient schemes is very crucial to address this problem. Our research considers the fixed window traffic allocation model and the anycast routing scheme to select the best option for the destination node. Proper routing schemes and appropriate combination of the replicas can take care of the issue for energy utilization and at the same time help diminish costs for the data centers. We have also considered the real-time pricing model (which considers price changes every hour) to select routes for the lightpaths. Hence, we propose an ILP to handle the energyaware routing and wavelength assignment (RWA) problem for fixed window scheduled traffic model, with an objective to minimize the overall electricity costs of a datacenter network by reducing the actual power consumption, and using low-cost resources whenever possible

Resilient virtual topologies in optical networks and clouds

Optical networks play a crucial role in the development of Internet by providing a high speed infrastructure to cope with the rapid expansion of high bandwidth demand applications such as video, HDTV, teleconferencing, cloud computing, and so on. Network virtualization has been proposed as a key enabler for the next generation networks and the future Internet because it allows diversification the underlying architecture of Internet and lets multiple heterogeneous network architectures coexist. Physical network failures often come from natural disasters or human errors, and thus cannot be fully avoided. Today, with the increase of network traffic and the popularity of virtualization and cloud computing, due to the sharing nature of network virtualization, one single failure in the underlying physical network can affect thousands of customers and cost millions of dollars in revenue. Providing resilience for virtual network topology over optical network infrastructure thus becomes of prime importance. This thesis focuses on resilient virtual topologies in optical networks and cloud computing. We aim at finding more scalable models to solve the problem of designing survivable logical topologies for more realistic and meaningful network instances while meeting the requirements on bandwidth, security, as well as other quality of service such as recovery time. To address the scalability issue, we present a model based on a column generation decomposition. We apply the cutset theorem with a decomposition framework and lazy constraints. We are able to solve for much larger network instances than the ones in literature. We extend the model to address the survivability problem in the context of optical networks where the characteristics of optical networks such as lightpaths and wavelength continuity and traffic grooming are taken into account. We analyze and compare the bandwidth requirement between the two main approaches in providing resiliency for logical topologies. In the first approach, called optical protection, the resilient mechanism is provided by the optical layer. In the second one, called logical restoration, the resilient mechanism is done at the virtual layer. Next, we extend the survivability problem into the context of cloud computing where the major complexity arises from the anycast principle. We are able to solve the problem for much larger network instances than in the previous studies. Moreover, our model is more comprehensive that takes into account other QoS criteria, such that recovery time and delay requirement

Self-organized backpressure routing for the wireless mesh backhaul of small cells

The ever increasing demand for wireless data services has given a starring role to dense small cell (SC) deployments for mobile networks, as increasing frequency re-use by reducing cell size has historically been the most effective and simple way to increase capacity. Such densification entails challenges at the Transport Network Layer (TNL), which carries packets throughout the network, since hard-wired deployments of small cells prove to be cost-unfeasible and inflexible in some scenarios. The goal of this thesis is, precisely, to provide cost-effective and dynamic solutions for the TNL that drastically improve the performance of dense and semi-planned SC deployments. One approach to decrease costs and augment the dynamicity at the TNL is the creation of a wireless mesh backhaul amongst SCs to carry control and data plane traffic towards/from the core network. Unfortunately, these lowcost SC deployments preclude the use of current TNL routing approaches such as Multiprotocol Label Switching Traffic Profile (MPLS-TP), which was originally designed for hard-wired SC deployments. In particular, one of the main problems is that these schemes are unable to provide an even network resource consumption, which in wireless environments can lead to a substantial degradation of key network performance metrics for Mobile Network Operators. The equivalent of distributing load across resources in SC deployments is making better use of available paths, and so exploiting the capacity offered by the wireless mesh backhaul formed amongst SCs. To tackle such uneven consumption of network resources, this thesis presents the design, implementation, and extensive evaluation of a self-organized backpressure routing protocol explicitly designed for the wireless mesh backhaul formed amongst the wireless links of SCs. Whilst backpressure routing in theory promises throughput optimality, its implementation complexity introduces several concerns, such as scalability, large end-to-end latencies, and centralization of all the network state. To address these issues, we present a throughput suboptimal yet scalable, decentralized, low-overhead, and low-complexity backpressure routing scheme. More specifically, the contributions in this thesis can be summarized as follows: We formulate the routing problem for the wireless mesh backhaul from a stochastic network optimization perspective, and solve the network optimization problem using the Lyapunov-driftplus-penalty method. The Lyapunov drift refers to the difference of queue backlogs in the network between different time instants, whereas the penalty refers to the routing cost incurred by some network utility parameter to optimize. In our case, this parameter is based on minimizing the length of the path taken by packets to reach their intended destination. Rather than building routing tables, we leverage geolocation information as a key component to complement the minimization of the Lyapunov drift in a decentralized way. In fact, we observed that the combination of both components helps to mitigate backpressure limitations (e.g., scalability,centralization, and large end-to-end latencies). The drift-plus-penalty method uses a tunable optimization parameter that weight the relative importance of queue drift and routing cost. We find evidence that, in fact, this optimization parameter impacts the overall network performance. In light of this observation, we propose a self-organized controller based on locally available information and in the current packet being routed to tune such an optimization parameter under dynamic traffic demands. Thus, the goal of this heuristically built controller is to maintain the best trade-off between the Lyapunov drift and the penalty function to take into account the dynamic nature of semi-planned SC deployments. We propose low complexity heuristics to address problems that appear under different wireless mesh backhaul scenarios and conditions..

Hierarchical network topographical routing

Within the last 10 years the content consumption model that underlies many of the assumptions about traffic aggregation within the Internet has changed; the previous short burst transfer followed by longer periods of inactivity that allowed for statistical aggregation of traffic has been increasingly replaced by continuous data transfer models. Approaching this issue from a clean slate perspective; this work looks at the design of a network routing structure and supporting protocols for assisting in the delivery of large scale content services. Rather than approaching a content support model through existing IP models the work takes a fresh look at Internet routing through a hierarchical model in order to highlight the benefits that can be gained with a new structural Internet or through similar modifications to the existing IP model. The work is divided into three major sections: investigating the existing UK based Internet structure as compared to the traditional Autonomous System (AS) Internet structural model; a localised hierarchical network topographical routing model; and intelligent distributed localised service models. The work begins by looking at the United Kingdom (UK) Internet structure as an example of a current generation technical and economic model with shared access to the last mile connectivity and a large scale wholesale network between Internet Service Providers (ISPs) and the end user. This model combined with the Internet Protocol (IP) address allocation and transparency of the wholesale network results in an enforced inefficiency within the overall network restricting the ability of ISPs to collaborate. From this model a core / edge separation hierarchical virtual tree based routing protocol based on the physical network topography (layers 2 and 3) is developed to remove this enforced inefficiency by allowing direct management and control at the lowest levels of the network. This model acts as the base layer for further distributed intelligent services such as management and content delivery to enable both ISPs and third parties to actively collaborate and provide content from the most efficient source

Contribution to the design of VANET routing protocols for realistic urban environments

One of the main concerns of the cities' administration is mobility management. In Intelligent Transportation Systems (ITS), pedestrians, vehicles and public transportation systems could share information and react to any situation in the city. The information sensed by vehicles could be useful for other vehicles and for the mobility authorities. Vehicular Ad hoc Networks (VANETs) make possible the communication between vehicles (V2I) and also between vehicles and fixed infrastructure (V2I) managed by the city's authorities. In addition, VANET routing protocols minimize the use of fixed infrastructure since they employ multi-hop V2V communication to reach reporting access points of the city. This thesis aims to contribute in the design of VANET routing protocols to enable reporting services (e.g., vehicular traffic notifications) in urban environments. The first step to achieve this global objective has been the study of components and tools to mimic a realistic VANET scenario. Moreover, we have analyzed the impact of the realism of each one of those components in the simulation results. Then, we have improved the Address Resolution procedure in VANETs by including it in the routing signaling messages. Our approach simplifies the VANET operation and increases the packet delivery ratio as consequence. Afterwards, we have tackled the issue of having duplicate packets in unicast communications and we have proposed routing filters to lower their presence. This way we have been able to increase the available bandwidth and reduce the average packet delay with a slight increase of the packet losses. Besides, we have proposed a Multi-Metric Map aware routing protocol (MMMR) that incorporates four routing metrics (distance, trajectory, vehicle density and available bandwidth) to take the forwarding decisions. With the aim of increasing the number of delivered packets in MMMR, we have developed a Geographical Heuristic Routing (GHR) algorithm. GHR integrates Tabu and Simulated Annealing heuristic optimization techniques to adapt its behavior to the specific scenario characteristics. GHR is generic because it could use any geographical routing protocol to take the forwarding decisions. Additionally, we have designed an easy to implement forwarding strategy based on an extended topology information area of two hops, called 2-hops Geographical Anycast Routing (2hGAR) protocol. Results show that controlled randomness introduced by GHR improves the default operation of MMMR. On the other hand, 2hGAR presents lower delays than GHR and higher packet delivery ratio, especially in high density scenarios. Finally, we have proposed two mixed (integer and linear) optimization models to detect the best positions in the city to locate the Road Side Units (RSUs) which are in charge of gathering all the reporting information generated by vehicles.Una de las principales preocupaciones en la administración de las ciudades es la gestión de la movilidad de sus vehículos, debido a los problemas de tráfico como atascos y accidentes. En los sistemas inteligentes de transporte (SIT), peatones, vehículos y transporte público podrán compartir información y adaptarse a cualquier situación que suceda en la ciudad. La información obtenida por los sensores de los vehículos puede ser útil para otros vehículos y para las autoridades de movilidad. Las redes ad hoc vehiculares (VANETs) hacen posible la comunicación entre los propios vehículos (V2V) y entre vehículos y la infraestructura fija de la red de la ciudad (V2I). Asimismo, los protocolos de encaminamiento para redes vehiculares minimizan el uso de infraestructura fija de red, ya que los protocolos de encaminamiento VANET emplean comunicaciones multisalto entre vehículos para encaminar los mensajes hasta los puntos de acceso de la red en la ciudad. El objetivo de esta tesis doctoral es contribuir en el diseño de protocolos de encaminamiento en redes ad hoc vehiculares para servicios de notificaciones (p.ej. reportes del estado del tráfico) en entornos urbanos. El primer paso para alcanzar este objetivo general ha sido el estudio de componentes y herramientas para simular un escenario realista de red ad hoc vehicular. Además, se ha analizado el impacto del nivel de realismo de cada uno de los componentes de simulación en los resultados obtenidos. Así también, se ha propuesto un mecanismo de resolución de direcciones automático y coherente para redes VANET a través del uso de los propios mensajes de señalización de los protocolos de encaminamiento. Esta mejora simplifica la operación de una red ad hoc vehicular y como consecuencia aumenta la tasa de recepción de paquetes. A continuación, se ha abordado el problema de la aparición inesperada de paquetes de datos duplicados en una comunicación punto a punto. Para ello, se ha propuesto el filtrado de paquetes duplicados a nivel del protocolo de encaminamiento. Esto ha producido un incremento del ancho disponible en el canal y una reducción del retardo medio en la trasmisión de un paquete, a costa de un mínimo aumento de la pérdida de paquetes. Por otra parte, hemos propuesto un protocolo de encaminamiento multi-métrica MMMR (Multi-Metric Map-aware Routing protocol), el cual incorpora cuatro métricas (distancia al destino, trayectoria, densidad de vehículos y ancho de banda) en las decisiones de encaminamiento. Con el objetivo de aumentar la tasa de entrega de paquetes en MMMR, hemos desarrollado un algoritmo heurístico de encaminamiento geográfico denominado GHR (Geographical Heuristic Routing). Esta propuesta integra las técnicas de optimización Tabu y Simulated Annealing, que permiten a GHR adaptarse a las características específicas del escenario. Adicionalmente, hemos propuesto 2hGAR (2-hops Geographical Anycast Routing), un protocolo de encaminamiento anycast que emplea información de la topología de red a dos saltos de distancia para tomar la decisión de encaminamiento de los mensajes. Los resultados muestran que la aleatoriedad controlada de GHR en su operación mejora el rendimiento de MMMR. Asimismo, 2hGAR presenta retardos de paquete menores a los obtenidos por GHR y una mayor tasa de paquetes entregados, especialmente en escenarios con alta densidad de vehículos. Finalmente, se han propuesto dos modelos de optimización mixtos (enteros y lineales) para detectar los mejores lugares de la ciudad donde ubicar los puntos de acceso de la red, los cuales se encargan de recolectar los reportes generados por los vehículos.Postprint (published version