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

Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing

Opportunities and Challenges of Joint Edge and Fog Orchestration

Pushing contents, applications, and network functions closer to end users is necessary to cope with the huge data volume and low latency required in future 5G networks. Edge and fog frameworks have emerged recently to address this challenge. Whilst the edge framework was more infrastructure focused and more mobile operator-oriented, the fog was more pervasive and included any node (stationary or mobile), including terminal devices. This article analyzes the opportunities and challenges to integrate, federate, and jointly orchestrate the edge and fog resources into a unified framework.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586

Emerging Edge Computing Technologies for Distributed Internet of Things (IoT) Systems

The ever-increasing growth in the number of connected smart devices and various Internet of Things (IoT) verticals is leading to a crucial challenge of handling massive amount of raw data generated from distributed IoT systems and providing real-time feedback to the end-users. Although existing cloud-computing paradigm has an enormous amount of virtual computing power and storage capacity, it is not suitable for latency-sensitive applications and distributed systems due to the involved latency and its centralized mode of operation. To this end, edge/fog computing has recently emerged as the next generation of computing systems for extending cloud-computing functions to the edges of the network. Despite several benefits of edge computing such as geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed in realizing edge computing technologies for future IoT systems. In this regard, this paper provides a holistic view on the current issues and effective solutions by classifying the emerging technologies in regard to the joint coordination of radio and computing resources, system optimization and intelligent resource management. Furthermore, an optimization framework for edge-IoT systems is proposed to enhance various performance metrics such as throughput, delay, resource utilization and energy consumption. Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of edge computing.Comment: 16 pages, 4 figures, 2 tables, submitted to IEEE Wireless Communications Magazin