Contention reduction and reliability enhancement for optical burst switching
Bibliographic record
Abstract
The rapid growth of the Internet and the advent of WDM in the second generation of optical network systems have led to the extensive use of optical resources available for switching and routing. Such growth has boosted the research activities that focus on the most efficient techniques to make better use of the enormous speed and bandwidth of all-optical networks. Following the trend of carrying IP traffic over WDM, many research initiatives started under two major strategies: Wavelength Routing and Optical Burst Switching. Wavelength Routing is a technique that allows the establishment of direct end-to-end light channels between two nodes, known as light-paths. Whereas Optical Burst Switching is a forwarding technique employed with a transparent optical backbone aiming to keep a large part of the information in the optical domain, and reduce the opto-electronic conversion overhead. Optical Burst Switching is an attractive hybrid approach between coarse-grain optical circuit switching and fine-grain optical packet switching. However, a major concern for OBS networks is contention on outgoing data channels, which can result in burst loss. Therefore, contention resolution is necessary in order to handle the case where more than one burst are destined to go out of the same output port at the same time. This work explores the contention problem that occurs in optical burst switching. In order to control the contention and pave the way to optical burst switching to be more suitable for Internet traffic, we propose to use OBS with segmentation where a burst is broken into many small (in size) segments. In case of contention we remove only the segments involved in the contention. We also propose a hybrid architecture where part of the wavelengths is used for OBS and the others are used for wavelength routing technique. The edge node can use both of them and send the data according to the class of traffic. Another solution consists of controlling the network load. Indeed, under heavy traffic, the loss in OBS increases. Therefore one needs to control the traffic and keep the network away from congestion. Here we propose a protocol that reports the losses in the network to the edge nodes, so they can adjust the traffic accordingly. We propose also another protocol to retransmit the lost bursts. (Abstract shortened by UMI.)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".