Applying Autonomy with Bandwidth Allocation Models
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 a...
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Veröffentlicht in: | International journal of communication systems 2016-09, Vol.29 (13), p.2028-2040 |
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creator | Freitas Reale, Rafael Martins, Romildo Martins, Joberto S B |
description | 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. G-BAM 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´s reconfiguration with distinct network traffic scenarios by using G-BAM. 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. |
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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. G-BAM 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´s reconfiguration with distinct network traffic scenarios by using G-BAM. 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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. G-BAM 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´s reconfiguration with distinct network traffic scenarios by using G-BAM. 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In brief, it is investigated the switching among " BAM behaviors " and BAM´s reconfiguration with distinct network traffic scenarios by using G-BAM. 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.</abstract><pub>Wiley</pub><doi>10.5281/zenodo.1287913</doi><orcidid>https://orcid.org/0000-0003-1310-9366</orcidid><orcidid>https://orcid.org/0000-0003-1310-9366</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Artificial Intelligence Computer Science Emerging Technologies Modeling and Simulation Networking and Internet Architecture |
title | Applying Autonomy with Bandwidth Allocation Models |
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