Evaluating Virtual Router Performance for a Pluralist Future Internet

Abstract—Internet Service Providers resist innovating in the network core, fearing that deploying a new protocol or service compromises the network operation and their profit, as a consequence. Therefore, a new Internet model, called Future Internet, which enables core innovation, must accommodate new protocols and services with the current scenario, isolating each protocol stack from others. Virtualization is the key technique that provides concurrent protocol stack capability to the Future Internet elements. In this paper, we evaluate the performance of three widespread virtualization tools, Xen, VMware, and OpenVZ, considering their use for router virtualization. We conduct experiments with benchmarking tools to measure the overhead introduced by virtualization in terms of memory, processor, network, and disk performance of virtual routers running on commodity hardware. We also evaluate the effects of the increasing number of virtual machines on Xen network virtualization mechanism. Our results show that Xen best fits virtual router requirements. Moreover, Xen fairly shares the network access among virtual routers, but needs further enhancement when multiple virtual machines simultaneously forward traffic. Keywords-Xen; OpenVZ; VMware; Hypervisor; Virtual Router I

Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges

The epidemic spread of fake news is a side effect of the expansion of social networks to circulate news, in contrast to traditional mass media such as newspapers, magazines, radio, and television. Human inefficiency to distinguish between true and false facts exposes fake news as a threat to logical truth, democracy, journalism, and credibility in government institutions. In this paper, we survey methods for preprocessing data in natural language, vectorization, dimensionality reduction, machine learning, and quality assessment of information retrieval. We also contextualize the identification of fake news, and we discuss research initiatives and opportunities

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. However, the energy consumption series forecasting is a challenge for statistical linear and Machine Learning (ML) techniques due to temporal fluctuations and the presence of linear and non-linear patterns. Traditional statistical techniques are able to model linear patterns, while obtaining poor results in forecasting the non-linear component of the time series. ML techniques are data-driven and can model non-linear patterns, but their feature selection process and parameter specification are a complex task. This paper proposes an Evolutionary Hybrid System (EvoHyS) which combines statistical and ML techniques through error series modeling. EvoHyS is composed of three phases: (i) forecast of the linear and seasonal component of the time series using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, (ii) forecast of the error series using an ML technique, and (iii) combination of both linear and non-linear forecasts from (i) and (ii) using a a secondary ML model. EvoHyS employs a Genetic Algorithm (GA) for feature selection and hyperparameter optimization in phases (ii) and (iii) aiming to improve its accuracy. An experimental evaluation was conducted using consumption energy data of a smart grid in a one-step-ahead scenario. The proposed hybrid system reaches statistically significant improvements when compared to other statistical, hybrid, and ML approaches from the literature utilizing well known metrics, such as Mean Squared Error (MSE)

Blockchain reputation-based consensus: A scalable and resilient mechanism for distributed mistrusting applications

Consensus mechanisms in blockchain applications allow mistrusting peers to agree on the global state of the chain. Most of the existing consensus mechanisms, however, are constrained by low efficiency and high energy consumption. In this paper, we propose the Blockchain Reputation-Based Consensus (BRBC) mechanism in which a node must have the reputation score higher than a given network trust threshold before being allowed to insert a new block in the chain. A randomly-selected set of judges monitors the behaviour of each node involved in the consensus and updates the node reputation score. Every cooperative behaviour results in a reward, and a non-cooperative or malicious behaviour results in a punishment. BRBC also uses the reputation score to revoke access to nodes with a reputation score below a given threshold. We present a security analysis, and we demonstrate that BRBC resists against a set of known attacks in the blockchain network. Finally, we simulate a blockchain network to assert the mechanism scalability and resilience to malicious actions in various network scenarios and different rates of malicious actions. The results show BRBC to be efficient to expel all nodes that acted with more than 50% of malicious actions