An efficient way to represent the processors and their connections in omega networks
The understanding of the structure of a network can be enhanced efficiently with distance-reliant parameters. The metric dimension is one such parameter with numerous variations and a rich source of literature. The subject of our study pertains to the metric dimension and a few of its variants for a...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447925000280 |
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| author | Savari Prabhu T. Jenifer Janany Paul Manuel |
| author_facet | Savari Prabhu T. Jenifer Janany Paul Manuel |
| author_sort | Savari Prabhu |
| collection | DOAJ |
| description | The understanding of the structure of a network can be enhanced efficiently with distance-reliant parameters. The metric dimension is one such parameter with numerous variations and a rich source of literature. The subject of our study pertains to the metric dimension and a few of its variants for a broadly used interconnection network-omega network. Omega networks provide a structured and scalable interconnect solution for distributed memory architectures. In large-scale data centers where massive amounts of data are processed and analyzed, omega networks are used in the network infrastructure to interconnect servers and storage systems. The key feature of omega networks is their ability to provide multiple disjoint paths between any pair of nodes in the network. This characteristic helps reduce congestion and improve overall system performance, especially in large-scale parallel computing environments where data communication is a critical factor. Along with metric dimension, we provide the exact values of edge metric dimension, fault-tolerant metric and edge metric dimensions for this widely known network. We also compare these parameters with those of other predominant interconnection networks, such as the butterfly, Beneš, and fractal cubic networks, and we observe that the omega network has significantly lower values for these parameters compared to the other networks. |
| format | Article |
| id | doaj-art-fc0a02b9949049c8b15a8763f803a1fa |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-fc0a02b9949049c8b15a8763f803a1fa2025-02-07T04:47:26ZengElsevierAin Shams Engineering Journal2090-44792025-03-01163103287An efficient way to represent the processors and their connections in omega networksSavari Prabhu0T. Jenifer Janany1Paul Manuel2Department of Mathematics, Rajalakshmi Engineering College, Thandalam, Chennai 602105, India; Corresponding author.Department of Mathematics, Rajalakshmi Engineering College, Thandalam, Chennai 602105, IndiaDepartment of Information Science, College of Life Sciences, Kuwait University, KuwaitThe understanding of the structure of a network can be enhanced efficiently with distance-reliant parameters. The metric dimension is one such parameter with numerous variations and a rich source of literature. The subject of our study pertains to the metric dimension and a few of its variants for a broadly used interconnection network-omega network. Omega networks provide a structured and scalable interconnect solution for distributed memory architectures. In large-scale data centers where massive amounts of data are processed and analyzed, omega networks are used in the network infrastructure to interconnect servers and storage systems. The key feature of omega networks is their ability to provide multiple disjoint paths between any pair of nodes in the network. This characteristic helps reduce congestion and improve overall system performance, especially in large-scale parallel computing environments where data communication is a critical factor. Along with metric dimension, we provide the exact values of edge metric dimension, fault-tolerant metric and edge metric dimensions for this widely known network. We also compare these parameters with those of other predominant interconnection networks, such as the butterfly, Beneš, and fractal cubic networks, and we observe that the omega network has significantly lower values for these parameters compared to the other networks.http://www.sciencedirect.com/science/article/pii/S2090447925000280Omega networkMetric basisFault-toleranceEdge metric basisTwins |
| spellingShingle | Savari Prabhu T. Jenifer Janany Paul Manuel An efficient way to represent the processors and their connections in omega networks Ain Shams Engineering Journal Omega network Metric basis Fault-tolerance Edge metric basis Twins |
| title | An efficient way to represent the processors and their connections in omega networks |
| title_full | An efficient way to represent the processors and their connections in omega networks |
| title_fullStr | An efficient way to represent the processors and their connections in omega networks |
| title_full_unstemmed | An efficient way to represent the processors and their connections in omega networks |
| title_short | An efficient way to represent the processors and their connections in omega networks |
| title_sort | efficient way to represent the processors and their connections in omega networks |
| topic | Omega network Metric basis Fault-tolerance Edge metric basis Twins |
| url | http://www.sciencedirect.com/science/article/pii/S2090447925000280 |
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