Orquestração Cognitiva e Dinâmica da Alocação de Recursos para Redes MPLS/DSTE
| Main Author: | Reale, Rafael Freitas |
|---|---|
| Format: | info publication-thesis |
| Terbitan: |
, 2019
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/3732119 |
Daftar Isi:
- Competition for network resources needs to be arbitrated according to the characteristics of each application or service to provide a satisfactory, transparent and cost-effective manner for users. Answering resource requests dynamically, that is, without prior allocation (scheduling) of resources for applications and services can allow their sharing for better use of the network. Due to the heterogeneity of user profiles, applications, and services, real-time response to resource requests tends to generate dynamic competition for resources across networks as a whole. Bandwidth allocation models have the attributes that allow you to define application classes and control the distribution of resources between classes intuitively. These models can be changed and / or recongured in order to optimize resource utilization to evolve their behaviors in line with the traffic profile and communication and quality requirements defined for the network. Defining resource arbitrage behavior that reflects better effciency of the communication and quality requirements set for the network in a given traffic profile is a complex task. This complexity can make human intervention a point of failure. Generally speaking, cognitive management systems are designed to handle complex tasks where human intervention can be a point of failure. These systems are able to autonomously configure themselves in response to changes or failures in accordance with business policies specified by administrators. This thesis proposes the cognitive and dynamic orchestration of BAM-based resource allocation (bandwidth) for MPLS / DSTE networks. To this end, it presents a new bandwidth allocation model (GBAM - Generalized Bandwidth Allocation Model) that generalizes the behaviors of classic models and allows new combinations of strategies in a configurable way at runtime; and a cognitive framework that dynamically learns and orchestrates GBAM behavior in line with the traffic profile and communication and network quality requirements.