Applying Autonomy with Bandwidth Allocation Models

TL;DR

This paper explores how autonomic management combined with generalized bandwidth allocation models (GBAM) can dynamically switch resource allocation strategies in MPLS networks to enhance performance and simplify network management.

Contribution

It introduces a framework for autonomic switching among BAM behaviors using GBAM, demonstrating improved network performance through simulation under various traffic scenarios.

Findings

Autonomic BAM switching improves link utilization.

Dynamic reconfiguration reduces preemption.

Framework adapts to different traffic conditions effectively.

Abstract

Bandwidth Allocation Models (BAMs) are resource allocation methods used for networks in general. BAMs are currently applied for handling resources such as bandwidth allocation in MPLS DS-TE networks (LSP setup). In general, BAMs defines resource restrictions by class and allocate the available resources on demand. This is frequently necessary to manage large and complex systems like routing networks. GBAM is a new generalized BAM that, by configuration, incorporates the behavior of existing BAMs (MAM, RDM, G-RDM and AllocTC-Sharing). In effect, any current available BAM behavior is reproduced by G-BAM by simply adjusting its configuration parameters. This paper focuses on investigating the applicability of using autonomy together with Bandwidth Allocation Models (BAMs) for improve performance and facilitating the management of MPLS DS-TE networks. It is investigated the applicability of BAM switching using a framework with autonomic characteristics. In brief, it is investigated the switching among BAM behaviors and BAM reconfiguration with distinct network traffic scenarios by using GBAM. Simulation results suggest that the autonomic switching of BAM behaviors based on high-level management rules (SLAs, QoS or other police) may result in improving overall network management and operational parameters such as link utilization and preemption.