Container network functions: bringing NFV to the network edge

In order to cope with the increasing network utilization driven by new mobile clients, and to satisfy demand for new network services and performance guarantees, telecommunication service providers are exploiting virtualization over their network by implementing network services in virtual machines, decoupled from legacy hardware accelerated appliances. This effort, known as NFV, reduces OPEX and provides new business opportunities. At the same time, next generation mobile, enterprise, and IoT networks are introducing the concept of computing capabilities being pushed at the network edge, in close proximity of the users. However, the heavy footprint of today's NFV platforms prevents them from operating at the network edge. In this article, we identify the opportunities of virtualization at the network edge and present Glasgow Network Functions (GNF), a container-based NFV platform that runs and orchestrates lightweight container VNFs, saving core network utilization and providing lower latency. Finally, we demonstrate three useful examples of the platform: IoT DDoS remediation, on-demand troubleshooting for telco networks, and supporting roaming of network functions

Sensor function virtualization to support distributed intelligence in the internet of things

It is estimated that-by 2020-billion devices will be connected to the Internet. This number not only includes TVs, PCs, tablets and smartphones, but also billions of embedded sensors that will make up the "Internet of Things" and enable a whole new range of intelligent services in domains such as manufacturing, health, smart homes, logistics, etc. To some extent, intelligence such as data processing or access control can be placed on the devices themselves. Alternatively, functionalities can be outsourced to the cloud. In reality, there is no single solution that fits all needs. Cooperation between devices, intermediate infrastructures (local networks, access networks, global networks) and/or cloud systems is needed in order to optimally support IoT communication and IoT applications. Through distributed intelligence the right communication and processing functionality will be available at the right place. The first part of this paper motivates the need for such distributed intelligence based on shortcomings in typical IoT systems. The second part focuses on the concept of sensor function virtualization, a potential enabler for distributed intelligence, and presents solutions on how to realize it

Real-Time Containers: A Survey

Container-based virtualization has gained a significant importance in a deployment of software applications in cloud-based environments. The technology fully relies on operating system features and does not require a virtualization layer (hypervisor) that introduces a performance degradation. Container-based virtualization allows to co-locate multiple isolated containers on a single computation node as well as to decompose an application into multiple containers distributed among several hosts (e.g., in fog computing layer). Such a technology seems very promising in other domains as well, e.g., in industrial automation, automotive, and aviation industry where mixed criticality containerized applications from various vendors can be co-located on shared resources. However, such industrial domains often require real-time behavior (i.e, a capability to meet predefined deadlines). These capabilities are not fully supported by the container-based virtualization yet. In this work, we provide a systematic literature survey study that summarizes the effort of the research community on bringing real-time properties in container-based virtualization. We categorize existing work into main research areas and identify possible immature points of the technology

Addressing the Challenges in Federating Edge Resources

This book chapter considers how Edge deployments can be brought to bear in a global context by federating them across multiple geographic regions to create a global Edge-based fabric that decentralizes data center computation. This is currently impractical, not only because of technical challenges, but is also shrouded by social, legal and geopolitical issues. In this chapter, we discuss two key challenges - networking and management in federating Edge deployments. Additionally, we consider resource and modeling challenges that will need to be addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and Paradigms; Editors Buyya, Sriram

Service Virtualisation of Internet-of-Things Devices: Techniques and Challenges

Service virtualization is an approach that uses virtualized environments to automatically test enterprise services in production-like conditions. Many techniques have been proposed to provide such a realistic environment for enterprise services. The Internet-of-Things (IoT) is an emerging field which connects a diverse set of devices over different transport layers, using a variety of protocols. Provisioning a virtual testbed of IoT devices can accelerate IoT application development by enabling automated testing without requiring a continuous connection to the physical devices. One solution is to expand existing enterprise service virtualization to IoT environments. There are various structural differences between the two environments that should be considered to implement appropriate service virtualization for IoT. This paper examines the structural differences between various IoT protocols and enterprise protocols and identifies key technical challenges that need to be addressed to implement service virtualization in IoT environments.Comment: 4 page

A software-defined network solution for managing fog computing resources in sensor networks

The fast growth of Internet-connected embedded devices raises new challenges for the traditional network design, such as scalability, diversity, and complexity. To endorse these challenges, this thesis suggests the aggregation of several emerging technologies: software-defined networking (SDN), fog computing, containerization and sensor virtualization. This thesis proposes, designs, implements and evaluates a new solution based on the emergent paradigm of SDN to efficiently manage virtualized resources located at the network edge in scenarios involving embedded sensor devices. The sensor virtualization through the containers provides agility, flexibility and abstraction for the data processing, being possible to summarize the huge amount of data produced by sensor devices. The proposed architecture uses a software-defined system, managed by a Ryu SDN controller, and a websocket broker written from scratch that analyses the messages sent to the controller and activates containers when required. Performance and functional tests were performed to assess the time required from activating the sensor containers to being able to communicate with them. The results were obtained by sending four ICMP packets. The best time response results were obtained by the proactive controller behavior mode, when compared to the hybrid and reactive modes. This thesis contributed to fill the gaps in the area of IoT or sensor networks, concerning the design and implementation of an architecture that performed on-demand activation of offline IoT fog computing resources by using an SDN controller and sensor virtualization through containers.O rápido crescimento de dispositivos embebidos conectados à Internet gera novos desafios para a arquitetura de rede tradicional, tais como escalabilidade, diversidade e complexidade. Para resolver estes desafios, esta tese sugere a agregação de diversas tecnologias emergentes: rede definida por software (SDN), contentores, computação na periferia e virtualização de sensores. Esta tese propõe, projeta, implementa e avalia uma nova solução baseada no paradigma emergente do SDN para gerir, de forma eficiente, recursos virtualizados que se localizam na periferia da rede, em cenários com sensores embebidos. A virtualização de sensores, através do uso de contentores, fornece agilidade, flexibilidade e abstração para processamento de dados, sendo possível a sumarização do grande volume de dados produzido pelos sensores. A arquitetura proposta usa um sistema definido por software, gerido por um controlador SDN Ryu, e um websocket broker escrito desde o zero, que analisa as mensagens enviadas ao controlador e ativa contentores quando necessário. Foram realizados testes funcionais e de desempenho de forma a ser possível avaliar o tempo necessário desde a ativação de um contentor de sensores até ser possível a comunicação com este. Os resultados foram obtidos através do envio de quatro pacotes ICMP. O melhor resultado foi obtido pelo modo de comportamento proativo do controlador, quando comparado aos modos híbrido e reativo. Esta tese contribuiu para preencher as lacunas na área de IoT ou redes de sensores, no que diz respeito ao desenho e implementação de uma arquitetura que executa a ativação sob pedido de recursos computacionais e periféricos de IoT quando estes se encontram desligados, através do uso de um controlador SDN e virtualização de sensores através de contentores

A microservice architecture for predictive analytics in manufacturing

Abstract This paper discusses on the design, development and deployment of a flexible and modular platform supporting smart predictive maintenance operations, enabled by microservices architecture and virtualization technologies. Virtualization allows the platform to be deployed in a multi-tenant environment, while facilitating resource isolation and independency from specific technologies or services. Moreover, the proposed platform supports scalable data storage supporting an effective and efficient management of large volume of Industry 4.0 data. Methodologies of data-driven predictive maintenance are provided to the user as-a-service, facilitating offline training and online execution of pre-trained analytics models, while the connection of the raw data to contextual information support their understanding and interpretation, while guaranteeing interoperability across heterogeneous systems. A use case related to the predictive maintenance operations of a robotic manipulator is examined to demonstrate the effectiveness and the efficiency of the proposed platform