A proposal for secured, efficient and scalable layer 2 network virtualisation mechanism

El contenidos de los capítulos 3 y 4 está sujeto a confidencialidad. 291 p.La Internet del Futuro ha emergido como un esfuerzo investigador para superar estas limitaciones identificadas en la actual Internet. Para ello es necesario investigar en arquitecturas y soluciones novedosas (evolutivas o rompedoras), y las plataformas de experimentación surgen para proporcionar un entorno realista para validar estas nuevas propuestas a gran escala.Debido a la necesidad de compartir la misma infraestructura y recursos para testear simultáneamente diversas propuestas de red, la virtualización de red es la clave del éxito. Se propone una nueva taxonomía para poder analizar y comparar las diferentes propuestas. Se identifican tres tipos: el Nodo Virtual (vNode), la Virtualización posibilitada por SDN (SDNeV) y el overlay.Además, se presentan las plataformas experimentales más relevantes, con un foco especial en la forma en la que cada una de ellas permite la investigación en propuestas de red, las cuales no cumplen todos estos requisitos impuestos: aislamiento, seguridad, flexibilidad, escalabilidad, estabilidad, transparencia, soporte para la investigación en propuestas de red. Por lo tanto, una nueva plataforma de experimentación ortogonal a la experimentación es necesaria.Las principales contribuciones de esta tesis, sustentadas sobre tecnología SDN y NFV, son también los elementos clave para construir la plataforma de experimentación: la Virtualización de Red basada en Prefijos de Nivel 2 (Layer 2 Prefix-based Network Virtualisation, L2PNV), un Protocolo para la Configuración de Direcciones MAC (MAC Address Configuration Protocol, MACP), y un sistema de Control de Acceso a Red basado en Flujos (Flow-based Network Access Control, FlowNAC).Como resultado, se ha desplegado en la Universidad del Pais Vasco (UPV/EHU) una nueva plataforma experimental, la Plataforma Activada por OpenFlow de EHU (EHU OpenFlow Enabled Facility, EHU-OEF), para experimentar y validar estas propuestas realizadas

A proposal for secured, efficient and scalable layer 2 network virtualisation mechanism

El contenidos de los capítulos 3 y 4 está sujeto a confidencialidad. 291 p.La Internet del Futuro ha emergido como un esfuerzo investigador para superar estas limitaciones identificadas en la actual Internet. Para ello es necesario investigar en arquitecturas y soluciones novedosas (evolutivas o rompedoras), y las plataformas de experimentación surgen para proporcionar un entorno realista para validar estas nuevas propuestas a gran escala.Debido a la necesidad de compartir la misma infraestructura y recursos para testear simultáneamente diversas propuestas de red, la virtualización de red es la clave del éxito. Se propone una nueva taxonomía para poder analizar y comparar las diferentes propuestas. Se identifican tres tipos: el Nodo Virtual (vNode), la Virtualización posibilitada por SDN (SDNeV) y el overlay.Además, se presentan las plataformas experimentales más relevantes, con un foco especial en la forma en la que cada una de ellas permite la investigación en propuestas de red, las cuales no cumplen todos estos requisitos impuestos: aislamiento, seguridad, flexibilidad, escalabilidad, estabilidad, transparencia, soporte para la investigación en propuestas de red. Por lo tanto, una nueva plataforma de experimentación ortogonal a la experimentación es necesaria.Las principales contribuciones de esta tesis, sustentadas sobre tecnología SDN y NFV, son también los elementos clave para construir la plataforma de experimentación: la Virtualización de Red basada en Prefijos de Nivel 2 (Layer 2 Prefix-based Network Virtualisation, L2PNV), un Protocolo para la Configuración de Direcciones MAC (MAC Address Configuration Protocol, MACP), y un sistema de Control de Acceso a Red basado en Flujos (Flow-based Network Access Control, FlowNAC).Como resultado, se ha desplegado en la Universidad del Pais Vasco (UPV/EHU) una nueva plataforma experimental, la Plataforma Activada por OpenFlow de EHU (EHU OpenFlow Enabled Facility, EHU-OEF), para experimentar y validar estas propuestas realizadas

End-to-end Mobile Network Slicing

Wireless networks have gone through several years of evolution until now and will continue to do so in order to cater for the varying needs of its users. These demands are expected to continue to grow even more in the future, both in size and variability. Hence, the 5G technology needs to consider these variabilities in service demands and potential data explosion which could accompany users’ demands at the core of its architecture. For 5G mobile network to handle these foreseen challenges, network slicing \cite{c13} is seen as a potential path to tread as its standardization is progressing. In light of the proposed 5G network architecture and to support and end-to-end mobile network slicing, we implemented radio access network (RAN) slicing over a virtualized evolved Node B (eNodeB) and ensured multiple core network slices could communicate through it successfully. Our results, challenges and further research path are presented in this thesis report

Advancing Operating Systems via Aspect-Oriented Programming

Operating system kernels are among the most complex pieces of software in existence to- day. Maintaining the kernel code and developing new functionality is increasingly compli- cated, since the amount of required features has risen significantly, leading to side ef fects that can be introduced inadvertedly by changing a piece of code that belongs to a completely dif ferent context. Software developers try to modularize their code base into separate functional units. Some of the functionality or “concerns” required in a kernel, however, does not fit into the given modularization structure; this code may then be spread over the code base and its implementation tangled with code implementing dif ferent concerns. These so-called “crosscutting concerns” are especially dif ficult to handle since a change in a crosscutting concern implies that all relevant locations spread throughout the code base have to be modified. Aspect-Oriented Software Development (AOSD) is an approach to handle crosscutting concerns by factoring them out into separate modules. The “advice” code contained in these modules is woven into the original code base according to a pointcut description, a set of interaction points (joinpoints) with the code base. To be used in operating systems, AOSD requires tool support for the prevalent procedu- ral programming style as well as support for weaving aspects. Many interactions in kernel code are dynamic, so in order to implement non-static behavior and improve performance, a dynamic weaver that deploys and undeploys aspects at system runtime is required. This thesis presents an extension of the “C” programming language to support AOSD. Based on this, two dynamic weaving toolkits – TOSKANA and TOSKANA-VM – are presented to permit dynamic aspect weaving in the monolithic NetBSD kernel as well as in a virtual- machine and microkernel-based Linux kernel running on top of L4. Based on TOSKANA, applications for this dynamic aspect technology are discussed and evaluated. The thesis closes with a view on an aspect-oriented kernel structure that maintains coherency and handles crosscutting concerns using dynamic aspects while enhancing de- velopment methods through the use of domain-specific programming languages

Study, evaluation and contributions to new algorithms for the embedding problem in a network virtualization environment

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change and to enable a new business model decoupling the network services from the underlying infrastructure. The problem of embedding virtual networks in a substrate network is the main resource allocation challenge in network virtualization and is usually referred to as the Virtual Network Embedding (VNE) problem. VNE deals with the allocation of virtual resources both in nodes and links. Therefore, it can be divided into two sub-problems: Virtual Node Mapping where virtual nodes have to be allocated in physical nodes and Virtual Link Mapping where virtual links connecting these virtual nodes have to be mapped to paths connecting the corresponding nodes in the substrate network. Application of network virtualization relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as VNE algorithms. This thesis proposes a set of contributions to solve the research challenges of the VNE that have not been tackled by the research community. To do that, it performs a deep and comprehensive survey of virtual network embedding. The first research challenge identified is the lack of proposals to solve the virtual link mapping stage of VNE using single path in the physical network. As this problem is NP-hard, existing proposals solve it using well known shortest path algorithms that limit the mapping considering just one constraint. This thesis proposes the use of a mathematical multi-constraint routing framework called paths algebra to solve the virtual link mapping stage. Besides, the thesis introduces a new demand caused by virtual link demands into physical nodes acting as intermediate (hidden) hops in a path of the physical network. Most of the current VNE approaches are centralized. They suffer of scalability issues and provide a single point of failure. In addition, they are not able to embed virtual network requests arriving at the same time in parallel. To solve this challenge, this thesis proposes a distributed, parallel and universal virtual network embedding framework. The proposed framework can be used to run any existing embedding algorithm in a distributed way. Thereby, computational load for embedding multiple virtual networks is spread across the substrate network Energy efficiency is one of the main challenges in future networking environments. Network virtualization can be used to tackle this problem by sharing hardware, instead of requiring dedicated hardware for each instance. Until now, VNE algorithms do not consider energy as a factor for the mapping. This thesis introduces the energy aware VNE where the main objective is to switch off as many network nodes and interfaces as possible by allocating the virtual demands to a consolidated subset of active physical networking equipment. To evaluate and validate the aforementioned VNE proposals, this thesis helped in the development of a software framework called ALgorithms for Embedding VIrtual Networks (ALEVIN). ALEVIN allows to easily implement, evaluate and compare different VNE algorithms according to a set of metrics, which evaluate the algorithms and compute their results on a given scenario for arbitrary parameters

Monitoring and analysis system for performance troubleshooting in data centers

It was not long ago. On Christmas Eve 2012, a war of troubleshooting began in Amazon data centers. It started at 12:24 PM, with an mistaken deletion of the state data of Amazon Elastic Load Balancing Service (ELB for short), which was not realized at that time. The mistake first led to a local issue that a small number of ELB service APIs were affected. In about six minutes, it evolved into a critical one that EC2 customers were significantly affected. One example was that Netflix, which was using hundreds of Amazon ELB services, was experiencing an extensive streaming service outage when many customers could not watch TV shows or movies on Christmas Eve. It took Amazon engineers 5 hours 42 minutes to find the root cause, the mistaken deletion, and another 15 hours and 32 minutes to fully recover the ELB service. The war ended at 8:15 AM the next day and brought the performance troubleshooting in data centers to world’s attention. As shown in this Amazon ELB case.Troubleshooting runtime performance issues is crucial in time-sensitive multi-tier cloud services because of their stringent end-to-end timing requirements, but it is also notoriously difficult and time consuming. To address the troubleshooting challenge, this dissertation proposes VScope, a flexible monitoring and analysis system for online troubleshooting in data centers. VScope provides primitive operations which data center operators can use to troubleshoot various performance issues. Each operation is essentially a series of monitoring and analysis functions executed on an overlay network. We design a novel software architecture for VScope so that the overlay networks can be generated, executed and terminated automatically, on-demand. From the troubleshooting side, we design novel anomaly detection algorithms and implement them in VScope. By running anomaly detection algorithms in VScope, data center operators are notified when performance anomalies happen. We also design a graph-based guidance approach, called VFocus, which tracks the interactions among hardware and software components in data centers. VFocus provides primitive operations by which operators can analyze the interactions to find out which components are relevant to the performance issue. VScope’s capabilities and performance are evaluated on a testbed with over 1000 virtual machines (VMs). Experimental results show that the VScope runtime negligibly perturbs system and application performance, and requires mere seconds to deploy monitoring and analytics functions on over 1000 nodes. This demonstrates VScope’s ability to support fast operation and online queries against a comprehensive set of application to system/platform level metrics, and a variety of representative analytics functions. When supporting algorithms with high computation complexity, VScope serves as a ‘thin layer’ that occupies no more than 5% of their total latency. Further, by using VFocus, VScope can locate problematic VMs that cannot be found via solely application-level monitoring, and in one of the use cases explored in the dissertation, it operates with levels of perturbation of over 400% less than what is seen for brute-force and most sampling-based approaches. We also validate VFocus with real-world data center traces. The experimental results show that VFocus has troubleshooting accuracy of 83% on average.Ph.D

Descoberta de recursos para sistemas de escala arbitrarias

Doutoramento em InformáticaTecnologias de Computação Distribuída em larga escala tais como Cloud, Grid, Cluster e Supercomputadores HPC estão a evoluir juntamente com a emergência revolucionária de modelos de múltiplos núcleos (por exemplo: GPU, CPUs num único die, Supercomputadores em single die, Supercomputadores em chip, etc) e avanços significativos em redes e soluções de interligação. No futuro, nós de computação com milhares de núcleos podem ser ligados entre si para formar uma única unidade de computação transparente que esconde das aplicações a complexidade e a natureza distribuída desses sistemas com múltiplos núcleos. A fim de beneficiar de forma eficiente de todos os potenciais recursos nesses ambientes de computação em grande escala com múltiplos núcleos ativos, a descoberta de recursos é um elemento crucial para explorar ao máximo as capacidade de todos os recursos heterogéneos distribuídos, através do reconhecimento preciso e localização desses recursos no sistema. A descoberta eficiente e escalável de recursos ´e um desafio para tais sistemas futuros, onde os recursos e as infira-estruturas de computação e comunicação subjacentes são altamente dinâmicas, hierarquizadas e heterogéneas. Nesta tese, investigamos o problema da descoberta de recursos no que diz respeito aos requisitos gerais da escalabilidade arbitrária de ambientes de computação futuros com múltiplos núcleos ativos. A principal contribuição desta tese ´e a proposta de uma entidade de descoberta de recursos adaptativa híbrida (Hybrid Adaptive Resource Discovery - HARD), uma abordagem de descoberta de recursos eficiente e altamente escalável, construída sobre uma sobreposição hierárquica virtual baseada na auto-organizaçãoo e auto-adaptação de recursos de processamento no sistema, onde os recursos computacionais são organizados em hierarquias distribuídas de acordo com uma proposta de modelo de descriçãoo de recursos multi-camadas hierárquicas. Operacionalmente, em cada camada, que consiste numa arquitetura ponto-a-ponto de módulos que, interagindo uns com os outros, fornecem uma visão global da disponibilidade de recursos num ambiente distribuído grande, dinâmico e heterogéneo. O modelo de descoberta de recursos proposto fornece a adaptabilidade e flexibilidade para executar consultas complexas através do apoio a um conjunto de características significativas (tais como multi-dimensional, variedade e consulta agregada) apoiadas por uma correspondência exata e parcial, tanto para o conteúdo de objetos estéticos e dinâmicos. Simulações mostram que o HARD pode ser aplicado a escalas arbitrárias de dinamismo, tanto em termos de complexidade como de escala, posicionando esta proposta como uma arquitetura adequada para sistemas futuros de múltiplos núcleos. Também contribuímos com a proposta de um regime de gestão eficiente dos recursos para sistemas futuros que podem utilizar recursos distribuíos de forma eficiente e de uma forma totalmente descentralizada. Além disso, aproveitando componentes de descoberta (RR-RPs) permite que a nossa plataforma de gestão de recursos encontre e aloque dinamicamente recursos disponíeis que garantam os parâmetros de QoS pedidos.Large scale distributed computing technologies such as Cloud, Grid, Cluster and HPC supercomputers are progressing along with the revolutionary emergence of many-core designs (e.g. GPU, CPUs on single die, supercomputers on chip, etc.) and significant advances in networking and interconnect solutions. In future, computing nodes with thousands of cores may be connected together to form a single transparent computing unit which hides from applications the complexity and distributed nature of these many core systems. In order to efficiently benefit from all the potential resources in such large scale many-core-enabled computing environments, resource discovery is the vital building block to maximally exploit the capabilities of all distributed heterogeneous resources through precisely recognizing and locating those resources in the system. The efficient and scalable resource discovery is challenging for such future systems where the resources and the underlying computation and communication infrastructures are highly-dynamic, highly-hierarchical and highly-heterogeneous. In this thesis, we investigate the problem of resource discovery with respect to the general requirements of arbitrary scale future many-core-enabled computing environments. The main contribution of this thesis is to propose Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. Operationally, at each layer, it consists of a peer-to-peer architecture of modules that, by interacting with each other, provide a global view of the resource availability in a large, dynamic and heterogeneous distributed environment. The proposed resource discovery model provides the adaptability and flexibility to perform complex querying by supporting a set of significant querying features (such as multi-dimensional, range and aggregate querying) while supporting exact and partial matching, both for static and dynamic object contents. The simulation shows that HARD can be applied to arbitrary scales of dynamicity, both in terms of complexity and of scale, positioning this proposal as a proper architecture for future many-core systems. We also contributed to propose a novel resource management scheme for future systems which efficiently can utilize distributed resources in a fully decentralized fashion. Moreover, leveraging discovery components (RR-RPs) enables our resource management platform to dynamically find and allocate available resources that guarantee the QoS parameters on demand