Evaluating Cbr Similarity Functions For Bam Switching In Networks With Dynamic Traffic Profile

Oliveira, Eliseu M.; Freitas, Rafael; Martins, Joberto S. B.

doi:10.5281/zenodo.1291127

Cited by 4 publications

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A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid

Calhau¹,

Martins

2019

2019 8th Brazilian Conference on Intelligent Systems (BRACIS)

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The complexity, heterogeneity and scale of electrical networks have grown far beyond the limits of exclusively human-based management at the Smart Grid (SG). Likewise, researchers cogitate the use of artificial intelligence and heuristics techniques to create cognitive and autonomic management tools that aim better assist and enhance SG management processes like in the grid reconfiguration. The development of self-healing management approaches towards a cognitive and autonomic distribution power network reconfiguration is a scenario in which the scalability and on-the-fly computation are issues. This paper proposes the use of Case-Based Reasoning (CBR) coupled with the HATSGA algorithm for the fast reconfiguration of large distribution power networks. The suitability and the scalability of the CBR-based reconfiguration strategy using HATSGA algorithm are evaluated. The evaluation indicates that the adopted HATSGA algorithm computes new reconfiguration topologies with a feasible computational time for large networks. The CBR strategy looks for managerial acceptable reconfiguration solutions at the CBR database and, as such, contributes to reduce the required number of reconfiguration computation using HATSGA. This suggests CBR can be applied with a fast reconfiguration algorithm resulting in more efficient, dynamic and cognitive grid recovery strategy.

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A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid

Calhau¹,

Martins

2019

2019 8th Brazilian Conference on Intelligent Systems (BRACIS)

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

Oliveira

Reale

Martins

2018

2018 IEEE Symposium on Computers and Communications (ISCC)

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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.

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A Methodological Approach to Model CBR-Based Systems

Oliveira¹,

Reale²,

Martins³

2020

JCC

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Artificial intelligence (AI) has been used in various areas to support system optimization and find solutions where the complexity makes it challenging to use algorithmic and heuristics. Case-based Reasoning (CBR) is an AI technique intensively exploited in domains like management, medicine, design, construction, retail and smart grid. CBR is a technique for problem-solving and captures new knowledge by using past experiences. One of the main CBR deployment challenges is the target system modeling process. This paper presents a straightforward methodological approach to model CBR-based applications using the concepts of abstract and concrete models. Splitting the modeling process with two models facilitates the allocation of expertise between the application domain and the CBR technology. The methodological approach intends to facilitate the CBR modeling process and to foster CBR use in various areas outside computer science.

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Evaluating Cbr Similarity Functions For Bam Switching In Networks With Dynamic Traffic Profile

Cited by 4 publications

References 0 publications

A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid

A Electric Network Reconfiguration Strategy with Case-Based Reasoning for the Smart Grid

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

A Methodological Approach to Model CBR-Based Systems

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