Breno Costa

Observability in distributed systems is the ability to understand the internal state of a system from its external data. Greater observability allows faster analysis of issues encountered at runtime, helping to meet agreed service levels. Fog Computing provides computational resources close to users and it is characterised by resource constraints and heterogeneity of devices and connections. This work proposes to define and characterise the challenges of increasing the observability of systems in a fog environment. For this, a real test environment was created and configured only with open source solutions. Through an Internet of Things use case, it was possible to measure the characteristic overhead of current observability solutions and discuss the challenges of such implementation in a Fog environment. A observabilidade em sistemas distribuídos é a capacidade de entender o estado interno de um sistema a partir de seus dados externos. Uma maior observabilidade permite rapidez na análise de problemas encontrados em tempo de execução, auxiliando a manutenção dos níveis de serviços acordados. Computação em Névoa fornece recursos computacionais próximos aos usuários e tem como características a restrição de recursos e a heterogeneidade dos dispositivos e conexões. Este trabalho se propõe a definir e caracterizar os desafios de aumentar a observabilidade de sistemas em um ambiente de Névoa. Para isso, um ambiente de testes real foi criado e configurado apenas com soluções de código aberto. Por meio de um caso de uso de Internet das Coisas, pôde-se medir a sobrecarga característica das soluções atuais de observabilidade e discutir acerca dos desafios desta implantação na Névoa.

Fog computing is a paradigm that allows the provisioning of computational resources and services at the edge of the network, closer to the end devices and users, complementing cloud computing. The heterogeneity and large number of devices are challenges to obtaining optimized resource allocation in this environment. Over time, some surveys have been presented on resource management in fog computing. However, they now lack a broader and deeper view about this subject, considering the recent publications. This article presents a systematic literature review with a focus on resource allocation for fog computing, and in a more comprehensive way than the existing works. The survey is based on 108 selected publications from 2012 to 2022. The analysis has exposed their main techniques, metrics used, evaluation tools, virtualization methods, architecture, and domains where the proposed solutions were applied. The results show an updated and comprehensive view about resource allocation in fog computing. The main challenges and open research questions are discussed, and a new fog computing resource management cycle is proposed.

A computação em névoa surgiu para atender à demanda por recursos computacionais mais próximos dos usuários finais, complementando assim a Computação em Nuvem. Por ser um paradigma emergente, existem várias questões em aberto, e desafios a serem superados; entre eles, o monitoramento, que objetiva coletar e manter atualizadas as informações do estado atual dos recursos computacionais e, ao mesmo tempo, dar suporte aos processos de gerenciamento da Computação em Névoa. Assim, este trabalho se propõe a apresentar testes que evidenciam a importância da Computação em Névoa na entrega de serviços que demandam baixa latência demonstrando, de maneira objetiva, a inadequação do uso da Computação em Nuvem neste contexto.

Fog computing is a distributed paradigm that provides computational resources in the users' vicinity. Fog orchestration is a set of functionalities that coordinate the dynamic infrastructure and manage the services to guarantee the Service Level Agreements. Monitoring is an orchestration functionality of prime importance. It is the basis for resource management actions, collecting status of resource and service and delivering updated data to the orchestrator. There are several cloud monitoring solutions and tools, but none of them comply with fog characteristics and challenges. Fog monitoring solutions are scarce, and they may not be prepared to compose an orchestration service. This paper updates the knowledge base about fog monitoring, assessing recent subjects in this context like observability, data standardization and instrumentation domains. We propose a novel taxonomy of fog monitoring solutions, supported by a systematic review of the literature. Fog monitoring proposals are analyzed and categorized by this new taxonomy, offering researchers a comprehensive overview. This work also highlights the main challenges and open research questions.

Fog computing is a paradigm that brings computational resources and services to the network edge in the vicinity of user devices, lowering latency and connecting with cloud computing resources. Unlike cloud computing, fog resources are based on constrained and heterogeneous nodes whose connectivity can be unstable. In this complex scenario, there is a need to define and implement orchestration processes to ensure that applications and services can be provided, considering the settled agreements. Although some publications have dealt with orchestration in fog computing, there are still some diverse definitions and functional intersection with other areas, such as resource management and monitoring. This article presents a systematic review of the literature with focus on orchestration in fog computing. A generic architecture of fog orchestration is presented, created from the consolidation of the analyzed proposals, bringing to light the essential functionalities addressed in the literature. This work also highlights the main challenges and open research questions.

Cloud computing is a new platform that offers potential cost reduction and reduced infrastructure management effort. Organizations are migrating legacy systems to the cloud to take advantage of its benefits. In this work, the authors propose a risk perception indicator (RPI) to bring objectivity to the decision about which systems could be migrated and about their migration order. The main goal of this article is to validate the RPI in the government domain. Through a case study with three government legacy systems and an experimental analysis based on a survey with 248 IT government employees, the RPI was evaluated and adjusted. The adjustments were made based on survey's results and appear to improve its accuracy and representativeness. The results showed that the use of the reference model and the risk perception indicator is sound and important regarding government legacy systems migration to the cloud. Request access from your librarian to read this article's full text.