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

Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein

Towards delay-aware container-based Service Function Chaining in Fog Computing

Recently, the fifth-generation mobile network (5G) is getting significant attention. Empowered by Network Function Virtualization (NFV), 5G networks aim to support diverse services coming from different business verticals (e.g. Smart Cities, Automotive, etc). To fully leverage on NFV, services must be connected in a specific order forming a Service Function Chain (SFC). SFCs allow mobile operators to benefit from the high flexibility and low operational costs introduced by network softwarization. Additionally, Cloud computing is evolving towards a distributed paradigm called Fog Computing, which aims to provide a distributed cloud infrastructure by placing computational resources close to end-users. However, most SFC research only focuses on Multi-access Edge Computing (MEC) use cases where mobile operators aim to deploy services close to end-users. Bi-directional communication between Edges and Cloud are not considered in MEC, which in contrast is highly important in a Fog environment as in distributed anomaly detection services. Therefore, in this paper, we propose an SFC controller to optimize the placement of service chains in Fog environments, specifically tailored for Smart City use cases. Our approach has been validated on the Kubernetes platform, an open-source orchestrator for the automatic deployment of micro-services. Our SFC controller has been implemented as an extension to the scheduling features available in Kubernetes, enabling the efficient provisioning of container-based SFCs while optimizing resource allocation and reducing the end-to-end (E2E) latency. Results show that the proposed approach can lower the network latency up to 18% for the studied use case while conserving bandwidth when compared to the default scheduling mechanism

Adaptive Process Distribution at the Edge of IoT using the Integration of BPMS and Containerization

Täna levivad pilvepõhised värkvõrgu (asjade interneti) süsteemid tuginevad protsesside halduseks kaugel asuvatel andmekeskustel, mis toob endaga kaasa latentsusprobleeme. Vastusena sellele probleemile on varem välja pakutud servaarvutuse lähenemine, kus arvutused viiakse läbi asjade interneti süsteemi võrgule füüsiliselt lähemal. Mitmete servaarvutuse metoodikate seas on uduarvutus lähenemine, kus rõhk on arvutuste liigutamisel värkvõrgu seadmetele endile. Ehkki uduarvutusel põhinev arhitektuur on paljutõotav, tõstatab see küsimuse – kuidas värkvõrgu protsessihaldussüsteemid (BPMS4IoT-süsteemid) äriprotsesse heterogeensetele värkvõrgu seadmetele jaotama peaksid? Levinud on lähenemine, kus protsesside töövooülesannete käituseks tuginetakse ühisele platvormile. Näiteks, kui haldusserver defineerib teatud töövoo ülesandena Pythoni skripti ja määrab selle seadmele, siis peab seadme töövookäitusmootor toetama vastavat mehhanismi skriptide jooksutamiseks. Selline nõue ei ole paindlik, arvestades värkvõrgu seadmete heterogeensust. Käesolevas magistritöös pakub autor välja raamistiku, mis eraldab töövoo ülesannete käitusmeetodi käitusmootorist kasutades selleks konteinertehnoloogiat. Töö käigus arendati välja raamistiku prototüüp ning viidi läbi katseid mikroarvutitel põhinevail seadmetel. Lisaks võrreldi väljapakutud uduarvutuse raamistiku jõudlust pilvearvutusel põhineva süsteemiga.Emerging cloud-centric Internet of Things (IoT) system relies on distant data centers to manage the entire processes, which raises the issue of latency. To address the issue, researchers have introduced the Edge computing methodologies that carry out computation closer to the edge network of IoT system. Among the numerous Edge computing approaches, Mist computing paradigm emphasises the mechanism that moves the computation further to the front-end IoT devices. Although the architecture of Mist computing is promising, it raises a new challenge in how the Business Process Management System for IoT (BPMS4IoT) distributes the business process workflow to the heterogeneous IoT devices? In general, executing business process workflows relies on the common platform for executing customized tasks. For example, if the management server defines a Python script task in a workflow, which has been allocated to an IoT device, the workflow engine of the IoT device must have the compatible execution method. Such a requirement is less flexible when one considers the heterogeneity of the IoT devices. Therefore, in this thesis, the author proposes a framework to decouple the workflow task execution method from the workflow engines using the containerization technology. A proof-of-concept prototype has been developed and has been tested on several single-board computers-based IoT devices. Further, a case study has been performed to demonstrate the performance of the proposed framework comparing to the cloud-centric system

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