Investigation of Bandwidth Reservation for Segment Routing
Bibliographic record
Abstract
Traffic Engineering (TE) is a critical topic in network routing and switching. The topic has been intensively investigated. New network architecture has also been proposed to improve TE, e.g., Multi-Protocol Label Switching (MPLS) architecture. MPLS has been widely used to in the past 15 years or so. However, the overhead associated with MPLS architecture is high, particularly the Resource Reservation Protocol (RSVP)-TE protocol used for signaling and path creation/maintenance. Segment Routing (SR) is a relatively new network solution to mitigate the high overhead issue of MPLS/RSVP- TE and it has gained increasing attention. SR for IPv6 (SRv6) has drawn a great deal of attention recently for efficient and flexible TE features. However, more research is still needed for SRv6-based bandwidth reservation for TE, as RSVP- TE used for bandwidth reservation is no longer part of SR. The objective of this paper is to develop bandwidth reservation algorithms for SR-based solutions and investigate the performance of those algorithms. The current focus is on depth-first search (DFS) and breath first search (BFS) bandwidth reservation algorithms. The preliminary outcomes show that BFS results in higher bandwidth usage, whereas DFS is more time efficient in path computations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".