Posicionamento de funções virtuais de rede com eficiência energética utilizando abordagens metaheurísticas

Concerns about reducing energy consumption in the sector of Information and Communication Technology has increasingly motivated the transition of traditional services to the clouds. In this context, Network Functions Virtualization (NFV) emerges as a solution to migrate various network functions, from dedicated hardware devices to a virtual environment based on commodity hardware. With this virtualization, in addition to the promise of increasing energy efficiency, it is expected to reduce the financial cost and increase the flexibility and scalability of the networks. In this research, it is proposed the development of algorithms based on three metaheuristics (Standard Hill-Climbing, Simulated Annealing, and Memetic Algorithm) to schedule network functions in cloud data centers, observing not only the capacities and energy consumption of the computers where the functions will be executed but also of the network and switches that connect these computers. Comparing the algorithms proposed in relation to the Best Fit algorithm found in the literature, the one based on Simulated Annealing saved 55.44% of energy consumption in a datacenter with Three-tier topology and the one based on memetic algorithm saved 49.18% of energy consumption in a data center with Fat-Tree topology. To allow the reproduction of all the experiments carried out in this research, the codes developed are publicly available as free softwareA preocupação em reduzir o consumo de energia elétrica no setor de tecnologias da informação e comunicação tem motivado cada vez mais a transição de serviços tradicionais dessa área para as nuvens. Nesse contexto, a virtualização de funções de rede (NFV Network Functions Virtual- ization) surge como uma solução para migrar várias funções de rede, de dispositivos de hardware dedicados, para um ambiente virtual baseado em máquinas de propósito geral. Com essa virtual- ização, além da promessa de aumento da eficiência energética, espera-se reduzir o custo financeiro e aumentar a flexibilidade e a escalabilidade das redes. Nesta pesquisa, é proposto o desenvolvimento de algoritmos baseados em três metaheurísticas (Hill-Climbing, Simulated Annealing e Algoritmo Memético) para escalonar funções de rede em data centers de nuvens, observando não apenas a capacidade e consumo de energia dos computadores onde as funções serão executadas mas também da rede e dos switches que interligam esses computadores. Comparando os algoritmos propostos em relação ao algoritmo Best Fit encontrado na literatura, o baseado em Simulated Annealing econo- mizou 55,44% do consumo de energia em um datacenter com topologia Three-tier e o baseado em algoritmo memético economizou 49,18% do consumo de energia em um datacenter com topologia Fat-Tree. Para permitir a fácil reprodução de todos os experimentos realizados nessa pesquisa, os códigos desenvolvidos estão disponibilizados publicamente como software livr

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research