Ambiente para Aquisicao de Conhecimento por Agentes em Dominios Restritos na Infraestrutura da Internet

This project establishes an environment for knowledge acquisition, learning, use and collaboration inter-agents over Internet Infrastructure. Four agent types are used in a previously applied four-tier model (A2RD), such as the use case on the Internet Routing Registry. This model, which can be implemented in each Autonomous System domain of the Internet infrastructure, is integrated into an environment with (a) capturing information from unstructured databases, (b) creating and updating training bases appropriate to machine learning algorithms and (c) creating and feeding of a knowledge base. Such resources become readily available to agents in each domain and to agents in all other domains with the aim of making them autonomous. The agents collaborate and interact with each other, through individual blockchain structures that also take care of operational security and integration aspects. In addition, a testbed to validate the entire model, including the functionalities of the agents, is also proposed and characterized.Acnowledge: This work is supported by CAPES -- Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Brazil’s Ministry of Education, and is also supported by national funds through FCT with reference UID/CEC/50021/2019, and is supported by MackenziPesquisa from Universidade Presbiteriana Mackenzie..

On Intelligent, Autonomous and Collaborative Agents to Manage Internet Routing Domains

This article describes an environment for knowledge acquisition, learning, use and collaboration inter agents over Internet Infrastructure. Four agent types are used in a previously applied fourtier model, such as the use case on the Internet Routing Registry. This model, which can be implemented in each Autonomous System domain of the Internet infrastructure, is integrated into an environment with (a) capturing information from unstructured databases, (b) creating and updating training bases appropriate to machine learning algorithms and (c) creation and feeding of a knowledge base. Such resources become readily available to agents in each domain and to agents in all other domains with the aim of making them autonomous. The agents collaborate and interact with each other, through individual blockchain structures that also take care of operational security and integration aspects. In addition, a test bed to validate the entire model, including the functionalities of the agents, is also proposed and characterized.

Performance Analysis of Structured, Un-Structured, and Cloud Storage Systems

Big Data management is an interesting research challenge for all storage vendors. Since data can be structured or unstructured, hence variety of storage systems has been designed to meet storage requirement as per organization's demands. The article focuses on different kinds of storage systems, their architecture and implementations. The first portion of the article describes different examples of structured (PostgreSQL) and unstructured databases (MongoDB, OrientDB and Neo4j) along with data models and comparative performance analysis between them. The second portion of the paper focuses on cloud storage systems. As an example of cloud storage, Google Cloud Storage and mainly its implementation details have been discussed. The aim of the article is not to eulogize any particular storage system, but to clearly point out that every storage has a role to play in the industry. It depends on the enterprise to identify the requirements and deploy the storage systems.

Handling Unstructured Databases on Cloud Using Hadoop

Unstructured database is defined as that in which we cannot store data in form of table i.e. rows and columns. So to handle that types of data we are going to store NoSQL i.e. KV (key value) store database. Everyone can handle big or huge data using Hadoop technology by creating a Hadoop cluster on cloud and also can perform CRUD operations on data. On cloud, we can store any type of data whether it is relational or non-relational data. This provides access of data to all users as it is open source and is implemented in java.