Next-generation optical networks to sustain connectivity of the future: All roads lead to optical-computing-enabled network?
Notice bibliographique
Résumé
The rise and then rapid developments of various nascent technologies, encompassing notably Internet of Things (IoT), Big Data and Artificial Intelligence (AI) have been heralding a new era of connectivity, spanning from people, things, to ultimately intelligence. Such connectivity of the future will be expected to drive explosive Internet traffic growths and thus, posing unprecedented challenges for network operators in scaling up the capacity in a greater cost and energy efficiency. Optical communications and networks constituting the backbone of Internet infrastructure will thus have to be radically different in the next 10 years and beyond. Indeed, there have been a number of on-going technological innovations holding the promises of order-of-magnitude capacity expansion, notably multi-band and/or spatial-division-multiplexing-based technologies. On the other hand, from an architectural perspective with the main goal of reducing the effective traffic load in the network and thus gaining greater operational efficiency, optical networks have been essentially remained unchanged in the recent two decades since the year 2000s with the success and then dominance of optical-bypass mode, featuring both significant cost and energy savings compared to the predecessor optical-electrical-optical operation. In the optical-bypass-enabled network, provisioning a lightpath involves the essential cross-connection function whose the underlying principle lies in the fact that in cross-connecting in-transit lightpaths over an intermediate node , such lightpaths must be guarded from each other in a certain dimension, be it the time, frequency or spatial domain, to avoid interference, which is treated as a destructive factor. In view of the rapid progresses in the realm of optical computing enabling the purposed interference between optical channels that are tailored to various computing capabilities, we envision a different perspective to turn around the long-established wisdom in optical-bypass network by putting the optical channel interference to a good use, resulting into the new operational paradigm, entitled, optical-computing-enabled network , weaving together optical communication and computing infrastructure. The optical-computing-enabled network is essentially characterized by the new capability at optical nodes permitting the superposition of transitional lightpaths to compute new ones of better spectrum utilization and/or for special computing purposes such as large-scale AI training. In underlining the potential merits of bringing in-network optical computing functions into the optical layer , this paper presents two illustrative examples based on the optical aggregation and optical XOR operations which have been progressively maturing and thus, could be feasibly integrated into the current legacy infrastructure with possibly minimal disruptions. As a departure from optical-bypass operation, the new optical computing capabilities available at the optical nodes imply a radical change in the network design problems and deriving the associated algorithmic solutions, which are broadly termed as optical network design and planning 2.0, so that the capital and operational efficiency could be fully unlocked. As a proof-of-efficiency for the new operational paradigm, we propose a detailed case study in formulating and solving the network coding-enabled optical networks, demonstrating the efficacy of the optical-computing-enabled network , and highlighting the unique challenges tied with greater complexities in network design problems, compared to optical-bypass counterpart.
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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,014 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
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Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».