# Cognitive Management of Bandwidth Allocation Models with Case-Based Reasoning -- Evidences Towards Dynamic BAM Reconfiguration

## TL;DR

This paper explores using Case-Based Reasoning to enable dynamic, cognitive reconfiguration of bandwidth allocation models in MPLS networks, improving resource utilization through self-adaptive management.

## Contribution

It introduces a novel CBR-based approach for autonomous BAM reconfiguration, enhancing network resource optimization in heterogeneous environments.

## Key findings

- CBR effectively learns bandwidth request patterns.
- Dynamic BAM switching improves resource utilization.
- Cognitive management enables self-configuration of BAMs.

## Abstract

Management is a complex task in today's heterogeneous and large scale networks like Cloud, IoT, vehicular and MPLS networks. Likewise, researchers and developers envision the use of artificial intelligence techniques to create cognitive and autonomic management tools that aim better assist and enhance the management process cycle. Bandwidth allocation models (BAMs) are a resource allocation solution for networks that need to share and optimize limited resources like bandwidth, fiber or optical slots in a flexible and dynamic way. This paper proposes and evaluates the use of Case-Based Reasoning (CBR) for the cognitive management of BAM reconfiguration in MPLS networks. The results suggest that CBR learns about bandwidth request profiles (LSPs requests) associated with the current network state and is able to dynamically define or assist in BAM reconfiguration. The BAM reconfiguration approach adopted is based on switching among available BAM implementations (MAM, RDM and ATCS). The cognitive management proposed allows BAMs self-configuration and results in optimizing the utilization of network resources.