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.
Bandwidth allocation model research has been focused on developing new models that, somehow, complement the basic ones: i) MAM proposed by @cite_16 , ii) RDM discussed in @cite_12 ; and iii) the AllocTCSharing (ATCS) model proposed by @cite_31 . The research group associated with this work has proposed 2 new basic BAM: i) the AllocTCSharing (ATCS) model @cite_31 ; and ii) the Generalized Bandwidth Allocation Model (GBAM) @cite_10 . Some additional hybrid models that fundamentally provide some extra capabilities to existing models have also been proposed by @cite_6 @cite_27 and @cite_8 .
@cite_8 @cite_6 @cite_27 @cite_31 @cite_16 @cite_10 @cite_12
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This paper proposes a new Bandwidth Constraints Model for MPLS networks, called CAM (Courteous Allocation Model). CAM allocates added bandwidth to lower classes of traffic without affecting the high priority traffic. Simulations are performed to compare CAM with the two basic Constraints Models, namely, MAM (Maximum Allocation Model) and RDM (Russian Dolls Model). The results show that our model gives significant improvement in terms of delay, throughput and packets loss. This paper proposes a decentralized model for the allocation of modulation and coding schemes, subchannels and transmit power to users in OFDMA femtocell deployments. The proposed model does not rely on any exchanged information between cells, which is especially useful for femtocell networks. Coordination between femtocells is achieved through the intrinsic properties of minimising transmit power independently at each cell, which leads the network to self-organize into an efficient frequency reuse pattern. This paper also provides a two-level decomposition approach for solving this intricate resource assignment problem that is able to find optimal solutions at cell level in reduced periods of time. System-level simulations show a significant performance improvement in terms of user outages and network capacity when using the proposed distributed resource allocation in comparison with scheduling techniques based on uniform power distributions among subcarriers. DiffServ-aware MPLS-TE (DS-TE) allows bandwidth reservation for Traffic Classes (TCs) in MPLS-based engineered networks and, as such, improves the basic MPLS-TE model. In DS-TE networks, per-Class quality of service guarantees are provided while being possible to achieve improved network utilization. DS-TE requires the use of a Bandwidth Allocation Model (BAM) that establishes the amount of bandwidth per-Class and any eventual sharing among them. This paper proposes a new bandwidth allocation model (AllocTC-Sharing) in which the higher priority traffic classes are allowed to use non allocated resources of lower priority traffic classes and vice versa. By adopting this “dual sense” allocation strategy for dynamic bandwidth allocation, it is shown that AllocTC-Sharing model preserves bandwidth constraints for traffic classes and improves overall link utilization. This paper investigates price-based resource allocation strategies for two-tier femtocell networks, in which a central macrocell is underlaid with distributed femtocells, all operating over the same frequency band. Assuming that the macrocell base station (MBS) protects itself by pricing the interference from femtocell users, a Stackelberg game is formulated to study the joint utility maximization of the macrocell and femtocells subject to a maximum tolerable interference power constraint at the MBS. Two practical femtocell network models are investigated: sparsely deployed scenario for rural areas and densely deployed scenario for urban areas. For each scenario, two pricing schemes: uniform pricing and non-uniform pricing, are proposed. The Stackelberg equilibriums for the proposed games are characterized, and an effective distributed interference price bargaining algorithm with guaranteed convergence is proposed for the uniform-pricing case. Numerical examples are presented to verify the proposed studies. It is shown that the proposed schemes are effective in resource allocation and macrocell protection for both the uplink and downlink transmissions in spectrum-sharing femtocell networks.