Building Dynamic and Energy-Efficient Datacenters with Passive Optical Devices
Bibliographic record
Abstract
Most datacenter network designs overwhelmingly use expensive and power-consuming electronic switches or expensive active optical switches with long reconfiguration times.In this thesis, we explore architectural solutions to leverage the design elements of Passive Optical Cross-Connection Networks with Multiple Planes (POXN/MPs) and Passive Optical Cross-Connection Networks with Multiple Planes and Bundled Ports (POXN/MP-BPs), both of which consist primarily of passive optical fabrics and optical transceivers that replace groups of switches in either hierarchical or recursively defined networks.Through simple physical interconnections, our proposed architectures allow datacenters to incrementally scale up in network capacity and scale out in total number of racks.From developed formulas for calculating cost and power consumption, we demonstrate that POXN/MP-BPs can significantly reduce the cost and power consumption of datacenter networks compared to the traditional fat tree topology.To lower overhead and adapt to the types of real datacenter scenarios that are possible with POXN/MP-BPs, we propose the new Multiple Channels with Bundled Ports Distributed Access Protocol (MCBDAP), which outperforms the Multiple Channels Distributed Access Protocol (MCDAP) for POXN/MP in terms of bandwidth efficiency, especially for those applications involving higher proportions of interrack traffic than intra-rack traffic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".