Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Fog Computing

Everything that is not a computer, in the traditional sense, is being connected to the Internet. These devices are also referred to as the Internet of Things and they are pressuring the current network infrastructure. Not all devices are intensive data producers and part of them can be used beyond their original intent by sharing their computational resources. The combination of those two factors can be used either to perform insight over the data closer where is originated or extend into new services by making available computational resources, but not exclusively, at the edge of the network. Fog computing is a new computational paradigm that provides those devices a new form of cloud at a closer distance where IoT and other devices with connectivity capabilities can offload computation. In this dissertation, we have explored the fog computing paradigm, and also comparing with other paradigms, namely cloud, and edge computing. Then, we propose a novel architecture that can be used to form or be part of this new paradigm. The implementation was tested on two types of applications. The first application had the main objective of demonstrating the correctness of the implementation while the other application, had the goal of validating the characteristics of fog computing.Tudo o que não é um computador, no sentido tradicional, está sendo conectado à Internet. Esses dispositivos também são chamados de Internet das Coisas e estão pressionando a infraestrutura de rede atual. Nem todos os dispositivos são produtores intensivos de dados e parte deles pode ser usada além de sua intenção original, compartilhando seus recursos computacionais. A combinação desses dois fatores pode ser usada para realizar processamento dos dados mais próximos de onde são originados ou estender para a criação de novos serviços, disponibilizando recursos computacionais periféricos à rede. Fog computing é um novo paradigma computacional que fornece a esses dispositivos uma nova forma de nuvem a uma distância mais próxima, onde “Things” e outros dispositivos com recursos de conectividade possam delegar processamento. Nesta dissertação, exploramos fog computing e também comparamos com outros paradigmas, nomeadamente cloud e edge computing. Em seguida, propomos uma nova arquitetura que pode ser usada para formar ou fazer parte desse novo paradigma. A implementação foi testada em dois tipos de aplicativos. A primeira aplicação teve o objetivo principal de demonstrar a correção da implementação, enquanto a outra aplicação, teve como objetivo validar as características de fog computing

Evaluation of the heat and energy performance of a datacenter by a new efficiency index: Energy Usage Effectiveness Design - EUED

Data Centers are growing steadily worldwide and they are expected to continue on growing up to 53% in 2020. Due to this growth the energy efficiency in this type of building is essential. There are methodologies to measure this efficiency; one example is PUE (Power Usage Effectiveness). The unit suggested for measuring efficiency at the design stage would be the EUED (Energy Usage Efficiency Design) with this will be used data to use "free cooling" and adiabatic system in some cases, a comparison will be made only considering the equipment in the worst situation. It also uses the study of enthalpy utilization as a new methodology to obtain the results. By doing so, differences were found, between cities than 1.21% of São Paulo in relation to Curitiba and 10.61% of Rio de Janeiro in relation to Curitiba. The indices obtained by applying the EUED index were 1.245 kW.kW- 1 for Curitiba, 1.260 kW.kW-1 for São Paulo and 1.377 kW.kW-1 for Rio de Janeiro, respectively, giving a difference of 16.86% for Curitiba of 16.19% for São Paulo and 10.31% for Rio de Janeiro in relation to PUE COA (Power Usage Effectiveness Constant Outdoor Air).info:eu-repo/semantics/publishedVersio

A first look into the carbon footprint of federated learning

Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in datacenters. In response, alternatives to centralized training such as Federated Learning (FL) have emerged. Perhaps unexpectedly, FL, in particular, is starting to be deployed at a global scale by companies that must adhere to new legal demands and policies originating from governments and civil society for privacy protection. However, the potential environmental impact related to FL remains unclear and unexplored. This paper offers the first-ever systematic study of the carbon footprint of FL. First, we propose a rigorous model to quantify the carbon footprint, hence facilitating the investigation of the relationship between FL design and carbon emissions. Then, we compare the carbon footprint of FL to traditional centralized learning. Our findings show that FL, despite being slower to converge in some cases, may result in a comparatively greener impact than a centralized equivalent setup. We performed extensive experiments across different types of datasets, settings, and various deep learning models with FL. Finally, we highlight and connect the reported results to the future challenges and trends in FL to reduce its environmental impact, including algorithms efficiency, hardware capabilities, and stronger industry transparency.Comment: arXiv admin note: substantial text overlap with arXiv:2010.0653

IoT*(Ambisense): Smart environment monitoring using LoRa

In this work, IoT* (AmbiSense), we present our developed IoT system as a solution for Building and Energy Management using visualization tools to identify heuristics and create automatic savings. Our developed prototypes communicate using LoRa, one of the latest IoT technologies, and are composed of a set of battery-operated sensors tied to a System on Chip. These sensors acquire environmental data such as temperature, humidity, luminosity, air quality, and also motion. For small to medium-size buildings where system management is possible, a multiplatform dashboard provides visualization templates with real-time data, allowing to identify patterns and extract heuristics that lead to savings using a set of pre-defined actions or manual intervention. LoBEMS (LoRa Building and Energy Management System), was validated in a kindergarten school during a three-year period. As an outcome, the evaluation of the proposed platform resulted in a 20% energy saving and a major improvement of the environment quality and comfort inside the school. For larger buildings where system management is not possible, we created a 3D visualization tool, that presents the system collected data and warnings in an interactive model of the building. This scenario was validated at ISCTE-IUL University Campus, where it was necessary to introduce the community interaction to achieve savings. As a requested application case, our system was also validated at the University Data Center, where the system templates were used to detect anomalies and suggest changes. Our flexible system approach can easily be deployed to any building facility without requiring large investments or complex system deployments.Nesta dissertação de mestrado, IoT * (AmbiSense), é apresentado um sistema IoT desenvolvido como uma solução para Gestão de Edifícios e Energia recorrendo a ferramentas de visualização para identificar heurísticas e criar poupanças automáticas. Os protótipos desenvolvidos comunicam utilizando LoRa, e são compostos por um conjunto de sensores ligados a um microcontrolador alimentado por bateria. Os sensores adquirem dados como temperatura, humidade, luminosidade, qualidade do ar e movimento. Para edifícios de pequena e média dimensão onde a gestão do sistema é possível, um dashboard fornece templates de visualização com dados em tempo real, permitindo extrair heurísticas, que introduzem poupanças através de um conjunto de ações predefinidas ou intervenção manual. O sistema LoBEMS (LoRa Building and Energy Management System), foi validado numa escola local durante um período de três anos. A avaliação do sistema resultou numa poupança de energia de 20% e uma melhoria significativa da qualidade do ambiente e conforto no interior da escola. Para edifícios de maior dimensão onde a gestão do sistema não é possível, criámos uma ferramenta de visualização 3D, que apresenta os dados e alertas do sistema, num modelo interativo do edifício. Este cenário foi validado no campus do ISCTE-IUL, onde foi necessária a interação da Comunidade para obter poupanças. Foi nos também solicitada uma validação do sistema no centro de dados da Universidade, onde os templates do sistema foram utilizados para detetar anomalias e sugerir alterações. A flexibilidade do sistema permite a sua implementação em qualquer edifício, sem exigir um grande investimento ou implementações complexas