Raciocínio Baseado em Casos para a Configuração de Modelos de Alocação de Banda

The extensive adoption of computer networks, especially the Internet, using services that require extensive data flows, has generated a growing demand for computational resources, mainly bandwidth. Bandwidth Allocation Models (BAM) have proven to be a viable alternative to network management where the bandwidth resource is shared to meet the high demand for the network. However, managing these networks has become an increasingly complex task, and solutions that allow for nearly autonomous configuration with less intervention of the network manager are highly demanded. The use of Case-Based Reasoning (CBR) techniques for network management has proven satisfactory for decision making and network management. This work presents a proposal for network reconfiguration based on the CBR cycle, intelligence, and cognitive module for MPLS (Multi-Protocol Label Switching) networks. The results show that CBR is a feasible solution for auto-configuration with autonomic characteristics in the MPLS using bandwidth allocation models (BAMs). The proposal improved the general network performance.

Self-Driving Networks

This chapter presents a new vision of network operations, the self-driving network, that takes automation to the next level. This is not a description of existing work; rather, it is a challenge to dramatically rethink how we manage networks (or rather, how we do not manage networks). It draws upon an analogy with the development of self-driving cars and presents motivations for this effort. It then describes the technologies needed to implement this and an overall architecture of the system. As this endeavor will cause a major shift in network management, the chapter offers an evolutionary path to the end goal. Some of the consequences and human impacts of such a system are touched upon. The chapter concludes with some research topics and a final message. Key takeaways are that machine learning and feedback loops are fundamental to the solution; a key outcome is to build systems that are adaptive and predictive, for the benefit of users.

A Simulation Model of the Confrontation between an Organized Attacker and an Information Security System in the Implementation of an Attack on a Network Management System of Clock Network Synchronization

This article provides an overview of the interaction between the warring parties and the main stages of the confrontation between the organized attacker and the information security system in the implementation of an attack on the network management system of clock network synchronization. A simulation model has been developed that reflects all stages of the struggle, which allows, depending on the resources of an organized attacker and the information security system, to obtain probabilistic and temporal characteristics of the results of the confrontation. Simulation has been carried out for various scenarios of organizing an attack at all stages of the confrontation, from the overwhelming advantage of an organized malefactor to the overwhelming advantage of an information security system. The results obtained in the general case can be used by security administrators and network administrators to make adjustments to the strategy of organizing the protection of the network management system of clock network synchronization.

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.

A Study of 5G Edge-Central Core Network Split Options

With the wide adoption of edge compute infrastructures, an opportunity has arisen to deploy part of the functionality at the edge of the network to enable a localized connectivity service. This development is also supported by the adoption of “on-premises” local 5G networks addressing the needs of different vertical industries and by new standardized infrastructure services such as Mobile Edge Computing (MEC). This article introduces a comprehensive set of deployment options for the 5G network and its network management, complementing MEC with the connectivity service and addressing different classes of use cases and applications. We have also practically implemented and tested the newly introduced options in the form of slices within a standard-based testbed. Our performed validation proved their feasibility and gave a realistic perspective on their impact. The qualitative assessment of the connectivity service gives a comprehensive overview on which solution would be viable to be deployed for each vertical market and for each large-scale operator situation, making a step forward towards automated distributed 5G deployments.

Traffic Refinery

Network management often relies on machine learning to make predictions about performance and security from network traffic. Often, the representation of the traffic is as important as the choice of the model. The features that the model relies on, and the representation of those features, ultimately determine model accuracy, as well as where and whether the model can be deployed in practice. Thus, the design and evaluation of these models ultimately requires understanding not only model accuracy but also the systems costs associated with deploying the model in an operational network. Towards this goal, this paper develops a new framework and system that enables a joint evaluation of both the conventional notions of machine learning performance (e.g., model accuracy) and the systems-level costs of different representations of network traffic. We highlight these two dimensions for two practical network management tasks, video streaming quality inference and malware detection, to demonstrate the importance of exploring different representations to find the appropriate operating point. We demonstrate the benefit of exploring a range of representations of network traffic and present Traffic Refinery, a proof-of-concept implementation that both monitors network traffic at 10~Gbps and transforms traffic in real time to produce a variety of feature representations for machine learning. Traffic Refinery both highlights this design space and makes it possible to explore different representations for learning, balancing systems costs related to feature extraction and model training against model accuracy.

Network Management System for IoT Based on Dynamic Systems

The Internet of Things (IoT) has the potential to transform the public sector by combining the leading technical and business trends of mobility, automation, and data analysis to dramatically alter the way public bodies collect data and information. Embedded sensors, actuators, and other devices that capture and transmit information about network activity in real-time are used in the Internet of Things to connect networks of physical objects. The design of a network management system for an IoT network is presented in this paper, which uses the edge computing model. This design is based on the Internet management model, which uses the SNMP protocol to communicate between managed devices, and a gateway, which uses the SOAP protocol to communicate with a management application. This work allowed for the identification and analysis of the primary network management system initiatives for IoT networks, in which there are four fundamental device management requirements for any deployment of IoT devices: provisioning and authentication, configuration and control, monitoring and diagnostics, and software updates and maintenance.