Provisioning mission-critical telerobotic control systems over internet backbone networks with essentially-perfect QoS
Bibliographic record
Abstract
Over the next decades, the Internet will evolve to support increasingly complex mission-critical services such as telerobotically controlled surgery. The world's first telerobotic surgery over the public Internet was performed in 2003, and since then several hundred more have been performed. Three critical requirements of these services include: (i) essentially 100% restoration capability, (ii) small and bounded end-toend queuing delays (ie < 250 millsec), and (iii) very low-jitter communications (ie < 10 millisec). In this paper, algorithms to provision mission-critical services over the Internet with essentially 100% restoration capability and essentially-perfect QoS are proposed, building upon two theoretical foundations. Mission-critical traffic is routed using the theory of shared backup protection paths or p-cycles, while background traffic is routed using multiple edge-disjoint paths. Mission-critical traffic is scheduled using the theory of recursive stochastic matrix decomposition to achieve two constraints: (i) near-minimal end to end queuing delay and jitter and (ii) essentially-perfect QoS. Designs of the Application-Specific Token-Bucket Traffic Shaper Queues (ASTSQs) and the Application-Specific Playback Queues (ASPQs) for telerobotic services are provided. To test the theory, extensive simulations of a saturated Internet backbone network supporting telerobotic services along with competing background traffic (ie VOIP, IPTV) are reported. It is shown that all missioncritical traffic can be delivered while meeting the three critical requirements, even in fully saturated backbone IP networks.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".