Fog Computing como arquitetura de rede distribuída para internet das coisas

Monografia (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2015.Fog Computing é uma extensão não trivial da Computação em Nuvem, que possibilita uma série de novos serviços e aplicações que não são completamente compatíveis com a arquitetura em nuvem. É apresentado o conceito de Fog Computing e como ele pode servir como arquitetura de rede para Internet das Coisas. Por meio de pesquisa bibliográfica, são mostrados princípios básicos para que o cliente final execute tarefas como: controle e configuração da rede; controle de HetNets; medidas e inferências de throughput para adaptação de taxa de transmissão; inferências sobre carga da rede por meios como active probing, correlação de performance e data mining; inferências no nível de aplicação usando DPI e análise de porta; pooling e caching de recursos com virtualização e cyber foraging. Também são apresentados aspectos de segurança e privacidade em Fog Computing e Internet das Coisas como segurança nas camadas de rede e em interfaces web.Through specific literature search , basic principles such as control and network configuration are shown; HetNets Control; throughput inferences and measure for the rate adaptation ; inferences about network load by means such as active probing, performance correlation and data mining; inferences on the application level using DPI and port analysis; pooling and caching capabilities with virtualization and cyber foraging. Security and privacy aspects of Fog Computing and Internet of Things are also presented

Cognitive Network Inference through Bayesian Network Analysis

Cognitive networking deals with applying cognition to the entire network protocol stack for achieving stack-wide as well as network-wide performance goals, unlike cognitive radios that apply cognition only at the physical layer. Designing a cognitive network is challenging since learning the relationship between network protocol parameters in an automated fashion is very complex. We propose to use Bayesian Network (BN) models for creating a representation of the dependence relationships among network protocol parameters. BN is a unique tool for modeling the network protocol stack as it not only learns the probabilistic dependence of network protocol parameters but also provides an opportunity to tune some of the cognitive network parameters to achieve desired performance. To the best of our knowledge, this is the first work to explore the use of BNs for cognitive networks. Creating a BN model for network parameters involves the following steps: sampling the network protocol parameters (Observe), learning the structure of the BN and its parameters from the data (Learn), using a Bayesian Network inference engine (Plan and Decide) to make decisions, and finally effecting the decisions (Act). We have proved the feasibility of achieving a BN-based cognitive network system using the ns-3 simulation platform. From the early results obtained from our cognitive network approach, we provide interesting insights on predicting the network behavior, including the performance of the TCP throughput inference engine based on other observed parameters