Towards a cloud enabler : from an optical network resource provisioning system to a generalized architecture for dynamic infrastructure services provisioning

This work was developed during a period where most of the optical management and provisioning system where manual and proprietary. This work contributed to the evolution of the state of the art of optical networks with new architectures and advanced virtual infrastructure services. The evolution of optical networks, and internet globally, have been very promising during the last decade. The impact of mobile technology, grid, cloud computing, HDTV, augmented reality and big data, among many others, have driven the evolution of optical networks towards current service technologies, mostly based on SDN (Software Defined Networking) architectures and NFV(Network Functions Virtualisation). Moreover, the convergence of IP/Optical networks and IT services, and the evolution of the internet and optical infrastructures, have generated novel service orchestrators and open source frameworks. In fact, technology has evolved that fast that none could foresee how important Internet is for our current lives. Said in other words, technology was forced to evolve in a way that network architectures became much more transparent, dynamic and flexible to the end users (applications, user interfaces or simple APIs). This Thesis exposes the work done on defining new architectures for Service Oriented Networks and the contribution to the state of the art. The research work is divided into three topics. It describes the evolution from a Network Resource Provisioning System to an advanced Service Plane, and ends with a new architecture that virtualized the optical infrastructure in order to provide coordinated, on-demand and dynamic services between the application and the network infrastructure layer, becoming an enabler for the new generation of cloud network infrastructures. The work done on defining a Network Resource Provisioning System established the first bases for future work on network infrastructure virtualization. The UCLP (User Light Path Provisioning) technology was the first attempt for Customer Empowered Networks and Articulated Private Networks. It empowered the users and brought virtualization and partitioning functionalities into the optical data plane, with new interfaces for dynamic service provisioning. The work done within the development of a new Service Plane allowed the provisioning of on-demand connectivity services from the application, and in a multi-domain and multi-technology scenario based on a virtual network infrastructure composed of resources from different infrastructure providers. This Service Plane facilitated the deployment of applications consuming large amounts of data under deterministic conditions, so allowing the networks behave as a Grid-class resource. It became the first on-demand provisioning system that at lower levels allowed the creation of one virtual domain composed from resources of different providers. The last research topic presents an architecture that consolidated the work done in virtualisation while enhancing the capabilities to upper layers, so fully integrating the optical network infrastructure into the cloud environment, and so providing an architecture that enabled cloud services by integrating the request of optical network and IT infrastructure services together at the same level. It set up a new trend into the research community and evolved towards the technology we use today based on SDN and NFV. Summing up, the work presented is focused on the provisioning of virtual infrastructures from the architectural point of view of optical networks and IT infrastructures, together with the design and definition of novel service layers. It means, architectures that enabled the creation of virtual infrastructures composed of optical networks and IT resources, isolated and provisioned on-demand and in advance with infrastructure re-planning functionalities, and a new set of interfaces to open up those services to applications or third parties.Aquesta tesi es va desenvolupar durant un període on la majoria de sistemes de gestió de xarxa òptica eren manuals i basats en sistemes propietaris. En aquest sentit, la feina presentada va contribuir a l'evolució de l'estat de l'art de les xarxes òptiques tant a nivell d’arquitectures com de provisió d’infraestructures virtuals. L'evolució de les xarxes òptiques, i d'Internet a nivell mundial, han estat molt prometedores durant l'última dècada. L'impacte de la tecnologia mòbil, la computació al núvol, la televisió d'alta definició, la realitat augmentada i el big data, entre molts altres, han impulsat l'evolució cap a xarxes d’altes prestacions amb nous serveis basats en SDN (Software Defined Networking) i NFV (Funcions de xarxa La virtualització). D'altra banda, la convergència de xarxes òptiques i els serveis IT, junt amb l'evolució d'Internet i de les infraestructures òptiques, han generat nous orquestradors de serveis i frameworks basats en codi obert. La tecnologia ha evolucionat a una velocitat on ningú podria haver predit la importància que Internet està tenint en el nostre dia a dia. Dit en altres paraules, la tecnologia es va veure obligada a evolucionar d'una manera on les arquitectures de xarxa es fessin més transparent, dinàmiques i flexibles vers als usuaris finals (aplicacions, interfícies d'usuari o APIs simples). Aquesta Tesi presenta noves arquitectures de xarxa òptica orientades a serveis. El treball de recerca es divideix en tres temes. Es presenta un sistema de virtualització i aprovisionament de recursos de xarxa i la seva evolució a un pla de servei avançat, per acabar presentant el disseny d’una nova arquitectura capaç de virtualitzar la infraestructura òptica i IT i proporcionar serveis de forma coordinada, i sota demanda, entre l'aplicació i la capa d'infraestructura de xarxa òptica. Tot esdevenint un facilitador per a la nova generació d'infraestructures de xarxa en el núvol. El treball realitzat en la definició del sistema de virtualització de recursos va establir les primeres bases sobre la virtualització de la infraestructura de xarxa òptica en el marc de les “Customer Empowered Networks” i “Articulated Private Networks”. Amb l’objectiu de virtualitzar el pla de dades òptic, i oferir noves interfícies per a la provisió de serveis dinàmics de xarxa. En quant al pla de serveis presentat, aquest va facilitat la provisió de serveis de connectivitat sota demanda per part de l'aplicació, tant en entorns multi-domini, com en entorns amb múltiples tecnologies. Aquest pla de servei, anomenat Harmony, va facilitar el desplegament de noves aplicacions que consumien grans quantitats de dades en condicions deterministes. En aquest sentit, va permetre que les xarxes es comportessin com un recurs Grid, i per tant, va esdevenir el primer sistema d'aprovisionament sota demanda que permetia la creació de dominis virtuals de xarxa composts a partir de recursos de diferents proveïdors. Finalment, es presenta l’evolució d’un pla de servei cap una arquitectura global que consolida el treball realitzat a nivell de convergència d’infraestructures (òptica + IT) i millora les capacitats de les capes superiors. Aquesta arquitectura va facilitar la plena integració de la infraestructura de xarxa òptica a l'entorn del núvol. En aquest sentit, aquest resultats van evolucionar cap a les tendències actuals de SDN i NFV. En resum, el treball presentat es centra en la provisió d'infraestructures virtuals des del punt de vista d’arquitectures de xarxa òptiques i les infraestructures IT, juntament amb el disseny i definició de nous serveis de xarxa avançats, tal i com ho va ser el servei de re-planificació dinàmicaPostprint (published version

Deliverable JRA1.1: Evaluation of current network control and management planes for multi-domain network infrastructure

This deliverable includes a compilation and evaluation of available control and management architectures and protocols applicable to a multilayer infrastructure in a multi-domain Virtual Network environment.The scope of this deliverable is mainly focused on the virtualisation of the resources within a network and at processing nodes. The virtualization of the FEDERICA infrastructure allows the provisioning of its available resources to users by means of FEDERICA slices. A slice is seen by the user as a real physical network under his/her domain, however it maps to a logical partition (a virtual instance) of the physical FEDERICA resources. A slice is built to exhibit to the highest degree all the principles applicable to a physical network (isolation, reproducibility, manageability, ...). Currently, there are no standard definitions available for network virtualization or its associated architectures. Therefore, this deliverable proposes the Virtual Network layer architecture and evaluates a set of Management- and Control Planes that can be used for the partitioning and virtualization of the FEDERICA network resources. This evaluation has been performed taking into account an initial set of FEDERICA requirements; a possible extension of the selected tools will be evaluated in future deliverables. The studies described in this deliverable define the virtual architecture of the FEDERICA infrastructure. During this activity, the need has been recognised to establish a new set of basic definitions (taxonomy) for the building blocks that compose the so-called slice, i.e. the virtual network instantiation (which is virtual with regard to the abstracted view made of the building blocks of the FEDERICA infrastructure) and its architectural plane representation. These definitions will be established as a common nomenclature for the FEDERICA project. Other important aspects when defining a new architecture are the user requirements. It is crucial that the resulting architecture fits the demands that users may have. Since this deliverable has been produced at the same time as the contact process with users, made by the project activities related to the Use Case definitions, JRA1 has proposed a set of basic Use Cases to be considered as starting point for its internal studies. When researchers want to experiment with their developments, they need not only network resources on their slices, but also a slice of the processing resources. These processing slice resources are understood as virtual machine instances that users can use to make them behave as software routers or end nodes, on which to download the software protocols or applications they have produced and want to assess in a realistic environment. Hence, this deliverable also studies the APIs of several virtual machine management software products in order to identify which best suits FEDERICA’s needs.Postprint (published version

MANTICORE II: IP Network as a Service Pilots at HEAnet, NORDUnet and RedIRIS

MANTICORE II follows the Infrastructure as a Service (IaaS) paradigm to enable National Research and Education Networks (NRENs) and other e-infrastructure providers to enhance their service portfolio by building and piloting the deployment of tools to provide infrastructure resources and IP networks as a service to virtual research communities. MANTICORE II is carrying out the following activities: * Robust and modular implementation of IaaS management tools. * Pilot software deployment and evaluation at HEAnet, NORDUnet and RedIRIS. * Design and implement a simple yet powerful graphical interface for the IP Network Service. * Study and simulate mechanisms to implement an infrastructure marketplace. * Study business models and use cases for commercial services based on MANTICORE II principles.Postprint (published version

분산 기계 학습의 자원 효율적인 수행을 위한 동적 최적화 기술

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 전병곤.Machine Learning(ML) systems are widely used to extract insights from data. Ever increasing dataset sizes and model complexity gave rise to many efforts towards efficient distributed machine learning systems. One of the popular approaches to support large scale data and complicated models is the parameter server (PS) approach. In this approach, a training job runs with distributed worker and server tasks, where workers iteratively compute gradients to update the global model parameters that are kept in servers. To improve the PS system performance, this dissertation proposes two solutions that automatically optimize resource efficiency and system performance. First, we propose a solution that optimizes the resource configuration and workload partitioning of distributed ML training on PS system. To find the best configuration, we build an Optimizer based on a cost model that works with online metrics. To efficiently apply decisions by Optimizer, we design our runtime elastic to perform reconfiguration in the background with minimal overhead. The second solution optimizes the scheduling of resources and tasks of multiple ML training jobs in a shared cluster. Specifically, we co-locate jobs with complementary resource use to increase resource utilization, while executing their tasks with fine-grained unit to avoid resource contention. To alleviate memory pressure by co-located jobs, we enable dynamic spill/reload of data, which adaptively changes the ratio of data between disk and memory. We build a working system that implements our approaches. The above two solutions are implemented in the same system and share the runtime part that can dynamically migrate jobs between machines and reallocate machine resources. We evaluate our system with popular ML applications to verify the effectiveness of our solutions.기계 학습 시스템은 데이터에 숨겨진 의미를 뽑아내기 위해 널리 사용되고 있다. 데이터셋의 크기와 모델의 복잡도가 어느때보다 커짐에 따라 효율적인 분산 기계 학습 시스템을위한 많은 노력들이 이루어지고 있다. 파라미터 서버 방식은 거대한 스케일의 데이터와 복잡한 모델을 지원하기 위한 유명한 방법들 중 하나이다. 이 방식에서, 학습 작업은 분산 워커와 서버들로 구성되고, 워커들은 할당된 입력 데이터로부터 반복적으로 그레디언트를 계산하여 서버들에 보관된 글로벌 모델 파 라미터들을 업데이트한다. 파라미터 서버 시스템의 성능을 향상시키기 위해, 이 논문에서는 자동적으로 자원 효율성과 시스템 성능을 최적화하는 두가지의 해법을 제안한다. 첫번째 해법은, 파라미터 시스템에서 분산 기계 학습을 수행시에 자원 설정 및 워크로드 분배를 자동화하는 것이다. 최고의 설정을 찾기 위해 우리는 온라인 메트릭을 사용하는 비용 모델을 기반으로 하는 Optimizer를 만들었다. Optimizer의 결정을 효율적으로 적용하기 위해, 우리는 런타임을 동적 재설정을 최소의 오버헤드로 백그라운드에서 수행하도록 디자인했다. 두번째 해법은 공유 클러스터 상황에서 여러 개의 기계 학습 작업의 세부 작업 과 자원의 스케쥴링을 최적화한 것이다. 구체적으로, 우리는 세부 작업들을 세밀한 단위로 수행함으로써 자원 경쟁을 억제하고, 서로를 보완하는 자원 사용 패턴을 보이는 작업들을 같은 자원에 함께 위치시켜 자원 활용율을 끌어올렸다. 함께 위치한 작업들의 메모리 압력을 경감시키기 위해 우리는 동적으로 데이터를 디스크로 내렸다가 다시 메모리로 읽어오는 기능을 지원함과 동시에, 디스크와 메모리간의 데이터 비율을 상황에 맞게 시스템이 자동으로 맞추도록 하였다. 위의 해법들을 실체화하기 위해, 실제 동작하는 시스템을 만들었다. 두가지의 해법을 하나의 시스템에 구현함으로써, 동적으로 작업을 머신 간에 옮기고 자원을 재할당할 수 있는 런타임을 공유한다. 해당 솔루션들의 효과를 보여주기 위해, 이 시스템을 많이 사용되는 기계 학습 어플리케이션으로 실험하였고 기존 시스템들 대비 뛰어난 성능 향상을 보여주었다.Chapter1. Introduction 1 1.1 Distributed Machine Learning on Parameter Servers 1 1.2 Automating System Conguration of Distributed Machine Learning 2 1.3 Scheduling of Multiple Distributed Machine Learning Jobs 3 1.4 Contributions 5 1.5 Dissertation Structure 6 Chapter2. Background 7 Chapter3. Automating System Conguration of Distributed Machine Learning 10 3.1 System Conguration Challenges 11 3.2 Finding Good System Conguration 13 3.2.1 Cost Model 13 3.2.2 Cost Formulation 15 3.2.3 Optimization 16 3.3 Cruise 18 3.3.1 Optimizer 19 3.3.2 Elastic Runtime 21 3.4 Evaluation 26 3.4.1 Experimental Setup 26 3.4.2 Finding Baselines with Grid Search 28 3.4.3 Optimization in the Homogeneous Environment 28 3.4.4 Utilizing Opportunistic Resources 30 3.4.5 Optimization in the Heterogeneous Environment 31 3.4.6 Reconguration Speed 32 3.5 Related Work 33 3.6 Summary 34 Chapter4 A Scheduling Framework Optimized for Multiple Distributed Machine Learning Jobs 36 4.1 Resource Under-utilization Problems in PS ML Training 37 4.2 Harmony Overview 42 4.3 Multiplexing ML Jobs 43 4.3.1 Fine-grained Execution with Subtasks 44 4.3.2 Dynamic Grouping of Jobs 45 4.3.3 Dynamic Data Reloading 54 4.4 Evaluation 56 4.4.1 Baselines 56 4.4.2 Experimental Setup 57 4.4.3 Performance Comparison 59 4.4.4 Performance Breakdown 59 4.4.5 Workload Sensitivity Analysis 61 4.4.6 Accuracy of the Performance Model 63 4.4.7 Performance and Scalability of the Scheduling Algorithm 64 4.4.8 Dynamic Data Reloading 66 4.5 Discussion 67 4.6 Related Work 67 4.7 Summary 70 Chapter5 Conclusion 71 5.1 Summary 71 5.2 Future Work 71 5.2.1 Other Communication Architecture Support 71 5.2.2 Deep Learning & GPU Resource Support 72 요약 81Docto

A framework for Traffic Engineering in software-defined networks with advance reservation capabilities

298 p.En esta tesis doctoral se presenta una arquitectura software para facilitar la introducción de técnicas de ingeniería de tráfico en redes definidas por software. La arquitectura ha sido diseñada de forma modular, de manera que soporte múltiples casos de uso, incluyendo su aplicación en redes académicas. Cabe destacar que las redes académicas se caracterizan por proporcionar servicios de alta disponibilidad, por lo que la utilización de técnicas de ingeniería de tráfico es de vital importancia a fin de garantizar la prestación del servicio en los términos acordados. Uno de los servicios típicamente prestados por las redes académicas es el establecimiento de circuitos extremo a extremo con una duración determinada en la que una serie de recursos de red estén garantizados, conocido como ancho de banda bajo demanda, el cual constituye uno de los casos de uso en ingeniería de tráfico más desafiantes. Como consecuencia, y dado que esta tesis doctoral ha sido co-financiada por la red académica GÉANT, la arquitectura incluye soporte para servicios de reserva avanzada. La solución consiste en una gestión de los recursos de red en función del tiempo, la cual mediante el empleo de estructuras de datos y algoritmos específicamente diseñados persigue la mejora de la utilización de los recursos de red a la hora de prestar este tipo de servicios. La solución ha sido validada teniendo en cuenta los requisitos funcionales y de rendimiento planteados por la red GÉANT. Así mismo, cabe destacar que la solución será utilizada en el despliegue piloto del nuevo servicio de ancho de banda bajo demanda de la red GÉANT a finales del 2017

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio

A framework for Traffic Engineering in software-defined networks with advance reservation capabilities

298 p.En esta tesis doctoral se presenta una arquitectura software para facilitar la introducción de técnicas de ingeniería de tráfico en redes definidas por software. La arquitectura ha sido diseñada de forma modular, de manera que soporte múltiples casos de uso, incluyendo su aplicación en redes académicas. Cabe destacar que las redes académicas se caracterizan por proporcionar servicios de alta disponibilidad, por lo que la utilización de técnicas de ingeniería de tráfico es de vital importancia a fin de garantizar la prestación del servicio en los términos acordados. Uno de los servicios típicamente prestados por las redes académicas es el establecimiento de circuitos extremo a extremo con una duración determinada en la que una serie de recursos de red estén garantizados, conocido como ancho de banda bajo demanda, el cual constituye uno de los casos de uso en ingeniería de tráfico más desafiantes. Como consecuencia, y dado que esta tesis doctoral ha sido co-financiada por la red académica GÉANT, la arquitectura incluye soporte para servicios de reserva avanzada. La solución consiste en una gestión de los recursos de red en función del tiempo, la cual mediante el empleo de estructuras de datos y algoritmos específicamente diseñados persigue la mejora de la utilización de los recursos de red a la hora de prestar este tipo de servicios. La solución ha sido validada teniendo en cuenta los requisitos funcionales y de rendimiento planteados por la red GÉANT. Así mismo, cabe destacar que la solución será utilizada en el despliegue piloto del nuevo servicio de ancho de banda bajo demanda de la red GÉANT a finales del 2017