Next-generation optical networks to sustain connectivity of the future: All roads lead to optical-computing-enabled network?
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".